Multimode sensor devices

ABSTRACT

The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type, the device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process and the BMD applies a time domain analysis on data provided by a sensor of the BMD when the data has a high signal (e.g., high signal-to-noise ratio), and applies a frequency domain analysis on the data when the data has a low signal, which contributes to improved accuracy and speed of biometric data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §119(e)(1) of U.S.Provisional Patent Application No. 61/800,095, filed Mar. 15, 2013,which is hereby incorporated by reference in its entirety.

BACKGROUND

Sensor devices can infer biometrics of interest from sensor data thatare associated with activities of a user. In many implementations ofsensor devices, however, the high accuracy of biometric estimates isachieved by limiting activity types and/or activity intensities that thesensor devices can monitor. For example, pedometers are recommended tobe worn on the left mid-axillary position for the most accurate stepcounts (Horvath et al. 2007). Even with the ideal placement location,pedometers can fail to provide reliable step counts, either byovercounting or undercounting steps in some activities such as busriding.

The placement of sensor devices is a significant constraint. Users ofsensor devices prefer to wear their portable sensor devices inconvenient locations. However, these convenient locations are often notideal for collecting biometric data. For example, the location of thesensor device may be remote from the body part or body parts that aremainly involved in the activity or have the strongest biometric signal.For this reason, current sensor devices sacrifice convenience foraccuracy or vice versa.

Recent advances in sensor, electronics, and power source miniaturizationhave allowed the size of personal health monitoring devices, alsoreferred to herein as “biometric tracking” or “biometric monitoring”devices, to be offered in small sizes. These biometric monitoringdevices may collect, derive, and/or provide one or more of the followingtypes of information: step counts, ambulatory speed, distance traveledcadence, heart rate, calorie burn, floors climbed and/or descended,location and/or heading, elevation, etc. However, the miniature size ofthe product limits the electric power it supplies. Therefore, there isthe need for energy saving methods and hardware that allow high speedand accurate computation of biometric information.

The inventions disclosed herein enable sensor devices to use one or moremodes to achieve computation speed and accuracy while maintaining energyefficiency.

SUMMARY

This disclosure enables sensor devices to use one or more modes. In someembodiments, different types of modes are run simultaneously. In otherembodiments, the most appropriate mode or set of modes is selected to beused at any one moment in time. These modes include, but are not limitedto different motion intensities, sensor device placement locations (e.g.where it is worn) and/or activity types. Automatically or manuallyswitching between the modes, the sensor devices track biometric datamore accurately regardless of the motion intensity, placement location,and/or activity type, while maintaining computation efficiency.

The disclosure provides BMDs that have multiple device modes dependingon operational conditions of the devices, e.g., motion intensity, deviceplacement, and/or activity type, the device modes are associated withvarious data processing algorithms. In some embodiments, methods fortracking physiological metrics using the BMDs are provided. In someembodiments, the process and the BMD applies a time domain analysis ondata provided by a sensor of the BMD when the data has a high signal(e.g., high signal-to-noise ratio), and applies a frequency domainanalysis on the data when the data has a low signal, which contributesto improved accuracy and speed of biometric data.

Some embodiments of the disclosure provide a method of tracking a user'sphysiological activity using a worn biometric monitoring device (BMD).The BMD has one or more sensors providing output data indicative of theuser's physiological activity. The method involves analyzing sensoroutput data provided by the biometric monitoring device to determinethat the output data has a relatively low signal-to-noise ratio (SNR)while the user is active. Upon the determination, the BMD collects thesensor output data for a duration sufficient to identify a periodiccomponent of the data. Then the BMD uses frequency domain analysis ofthe collected sensor output data to process and/or identify saidperiodic component. The BMD determines a metric of the user'sphysiological activity from the periodic component of the collectedsensor output data. Finally, the BMD may present the metric of theuser's physiological activity. In some embodiments, the one or moresensors of the BMD include a motion sensor, and the output data includesmotion intensity from the motion sensor. In some embodiments, the wornbiometric monitoring device includes a wrist-worn or arm-worn device.

Some embodiments of the disclosure provide a method of tracking a user'sphysiological activity using a worn biometric monitoring device (BMD).The method includes the following operations: (a) analyzing sensoroutput data provided by the biometric monitoring device to determinethat the user is engaged in a first activity that produces a relativelyhigh SNR in the sensor output data; (b) quantifying a physiologicalmetric by analyzing a first set of sensor output data in the timedomain; (c) analyzing subsequent sensor output data provided by thebiometric monitoring device to determine that the user is engaged in asecond activity that produces a relatively low SNR in the subsequentsensor output data; and (d) quantifying the physiological metric from aperiodic component of a second set of sensor output data by processingthe second set of sensor output data using a frequency domain analysis.For instance, the first activity may be running with hands movingfreely. The second activity may be walking when pushing a stroller. Insome embodiments, the frequency domain analysis includes one or more ofthe following: Fourier transform, cepstral transform, wavelet transform,filterbank analysis, power spectral density analysis and/or periodogramanalysis.

In some embodiments, the quantifying operation in (d) requires morecomputation per unit of the sensor output data duration than thequantifying in (b). In some embodiments, the quantifying in (d) requiresmore computation per unit of the physiological metric than thequantifying in (b).

In some embodiments, (b) and (d) each involves: identifying a periodiccomponent from the sensor output data; determining the physiologicalmetric from the periodic component of the sensor output data; andpresenting the physiological metric.

In some embodiments, the sensor output data include raw data directlyobtained from the sensor without preprocessing. In some embodiments, thesensor output data include data derived from the raw data afterpreprocessing.

In some embodiments, the worn biometric monitoring device is awrist-worn or arm-worn device.

In some embodiments, the operation of analyzing sensor output data in(a) or (c) involves characterizing the output data based on the signalnorms, signal energy/power in certain frequency bands, wavelet scaleparameters, and/or a number of samples exceeding one or more thresholds.

In some embodiments, the process further involves analyzing biometricinformation previously stored on the biometric monitoring device todetermine that the user is engaged in the first or the second activity.

In some embodiments, the one or more sensors include a motion sensor,wherein analyzing sensor output data in (a) or (c) involves using motionsignal to determine whether the user is engaged in the first activity orthe second activity. In some embodiments, the first activity involvesfree motion of a limb wearing the biometric monitoring device duringactivity. In some embodiments, the second activity comprises reducedmotion of the limb wearing the biometric monitoring device duringactivity. In some embodiments, the second activity involves the userholding a substantially non-accelerating object with a limb wearing thebiometric monitoring device.

In some embodiments, analyzing the first set of sensor output data inthe time domain involves applying peak detection to the first set ofsensor output data. In some embodiments, analyzing the second set ofsensor output data involves identifying a periodic component of thesecond set of sensor output data. In some embodiments, the first set ofsensor output data includes data from only one axis of a multi-axismotion sensor, wherein the second set of sensor output data include datafrom two or more axis of the multi-axis motion sensor.

In some embodiments, the frequency domain analysis involves frequencyband passing time domain signal, and then applying a peak detection inthe time domain. In some embodiments, the frequency domain analysisincludes finding any spectral peak/peaks that is/are a function of theaverage step rate. In some embodiments, the frequency domain analysisinvolves performing a Fisher's periodicity test. In some embodiments,the frequency domain analysis includes using a harmonic to estimateperiod and/or test periodicity. In some embodiments, the frequencydomain analysis include performing a generalized likelihood ratio testwhose parametric models incorporate harmonicity of motion signal.

Some embodiments further involve analyzing sensor output data toclassify motion signals into two categories: signals generated fromsteps and signals generated from activities other than steps.

In some embodiments, the physiological metric provided by the BMDincludes a step count. In some embodiments, the physiological metricincludes a heart rate. In some embodiments, the physiological metricincludes number of stairs climbed, calories burnt, and/or sleep quality.

Some embodiments further involves applying a classifier to the sensoroutput data and the subsequent sensor output data to determine theplacement of the biometric monitoring device on the user. In someembodiments, the processing in (b) comprises using information regardingthe placement of the biometric monitoring device to determine the valueof the physiological metric.

Some embodiments further include applying a classifier to the sensoroutput data and the subsequent sensor output data to determine whetherthe user is engaged in the first activity and/or the second activity. Insome embodiments, the first activity is one of the following: running,walking, elliptical machine, stair master, cardio exercise machines,weight training, driving, swimming, biking, stair climbing, and rockclimbing. In some embodiments, the processing in (b) includes usinginformation regarding activity type to determine the value of thephysiological metric.

Some embodiments provide a method of tracking a user's physiologicalactivity using a worn BMD, the method involves: (a) determining that theuser is engaged in a first type of activity by detecting a firstsignature signal in sensor output data, the first signature signal beingselectively associated with the first type of activity; (b) quantifyinga first physiological metric for the first type of activity from a firstset of sensor output data; (c) determining that the user is engaged in asecond type of activity by detecting a second signature signal in sensoroutput data, the second signature signal being selectively associatedwith the second type of activity and different from the first signaturesignal; and (d) quantifying a second physiological metric for the secondtype of activity from a second set of sensor output data. In someembodiments, the first signature signal and the second signature signalinclude motion data. In some embodiments, the first signature signal andthe second signature signal further include one or more of thefollowing: location data, pressure data, light intensity data, and/oraltitude data.

Some embodiments provide a BMD that includes one or more sensorsproviding sensor output data comprising information about a user'sactivity level when the biometric monitoring device is worn by the user.The BMD also includes control logic configured to: (a) analyze sensoroutput data to characterize the output data as indicative of a firstactivity associated with a relatively high signal level or indicative ofa second activity associated with a relatively low signal level; (b)process the sensor output data indicative of the first activity toproduce a value of a physiological metric; and (c) process the sensoroutput data indicative of the second activity to produce a value of thephysiological metric. In some embodiments, the processing of (b)requires more computation per unit of the physiological metric than theprocessing of (c).

Some embodiments provide a BMD having control logic that is configuredto: (a) analyzing sensor output data provided by the biometricmonitoring device to determine that the user is engaged in a firstactivity that produces a relatively high SNR in the sensor output data;(b) quantifying a physiological metric by analyzing the sensor outputdata in the time domain; (c) analyzing subsequent sensor output dataprovided by the biometric monitoring device to determine that the useris engaged in a second activity that produces a relatively low SNR inthe subsequent sensor output data; and (d) quantifying the physiologicalmetric from a periodic component of the subsequent sensor output data byprocessing the subsequent sensor output data using a frequency domainanalysis. In some embodiments, the analyzing in (d) requires morecomputation per unit of the physiological metric than the analyzing in(b).

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Note thatthe relative dimensions of the following figures may not be drawn toscale unless specifically indicated as being scaled drawings.

These and other implementations are described in further detail withreference to the Figures and the detailed description below.

BRIEF DESCRIPTION OF THE DRAWINGS

The various implementations disclosed herein are illustrated by way ofexample, and not by way of limitation, in the figures of theaccompanying drawings, in which like reference numerals may refer tosimilar elements.

FIG. 1 shows an example of a portable biometric monitoring device havinga button and a display according to some embodiments of the disclosure.

FIG. 2 shows an example of a wrist-watch like biometric monitoringdevice according to some embodiments of the disclosure.

FIG. 3 shows a flow chart of a method for tracking a user'sphysiological activity according to some embodiments.

FIG. 4A shows acceleration data in time domain (top panel) and frequencydomain (bottom panel) for stationary, walking, and running activity fora user. FIG. 4B shows similar data for stationary, running with hands onbars, and running with free hands.

FIG. 5A is a flowchart showing a process for tracking step count using aBMD according to some embodiments. FIG. 5B shows a process fordetermining three ranges of motion intensity modes according to someembodiments.

FIG. 6A is a flowchart showing a process to implement peak detection tocalculate step count under an active mode according to some embodiments.FIG. 6B is a flowchart showing a process that may be used to implementpeak detection according to some embodiments. FIG. 6C is a flowchartshowing a process for analyzing data in frequency domain under asemi-active mode according to some embodiments. FIG. 6D is a flowchartshowing a process that may be used to implement a spectral analysisaccording to some embodiments.

FIG. 7 depicts a generalized schematic of an example of a portablebiometric monitoring device or other device that may implement themultimode functions described herein.

DETAILED DESCRIPTION Introduction

Sensor devices or Biometric Monitoring Devices (BMDs) according toembodiments described herein typically have shapes and sizes that aresuitable for being coupled to (e.g., secured to, worn, borne by, etc.)the body or clothing of a user. BMDs are also referred to as biometrictracking devices herein. The devices collect one or more types ofphysiological and/or environmental data from embedded sensors and/orexternal devices.

In many applications, users of BMDs prefer to wear the BMD on theirwrists. Therefore, in some embodiments, BMDs are implemented aswatch-like, wrist-worn devices. Although many activity signatures arepresent in data obtained from the wrist or the arm, the data getinherently corrupted by unwanted motion and ambient noise. This leads tochallenges in trying to infer certain user activities such as steps byusing data obtained from the sensor device worn on the wrist. Thisdisclosure provides solution to this problem by providing multiple modesto ease the inference problem. Some embodiments use automated methods todetermine the modes. Some embodiments use user inputs to determine themodes. Various embodiments provide different data processing algorithmssuitable for different user activities and conditions.

BMDs are typically quite small due to practical considerations. Peoplewho wish to monitor their performance are unlikely to want to wear alarge, bulky device that may interfere with their activities or that maylook unsightly. As a result, biometric monitoring devices are oftenprovided in small form factors to allow for light weight and ease ofcarrying. Such small form factors often necessitate some designcompromises. For example, there may be limited space for displays,controls, and other components of the biometric monitoring device withinthe device housing. One system component that may be limited in size orperformance is the power source, e.g., a battery, capacitor, etc., ofthe biometric monitoring device. In many implementations, the biometricmonitoring device may be in an “always on” state to allow it tocontinually collect biometric data throughout the day and night. Giventhat the sensors and processor(s) of the biometric monitoring devicemust generally remain powered to some degree in order to collect thebiometric data, it may be advantageous to implement power-savingfeatures elsewhere in the device, e.g., such as by causing the displayto automatically turn off after a period of time, or by measuringcertain data such as heart rate data momentarily on demand indicated bya user-gesture. A typical user gesture may be provided by pressing abutton on the biometric monitoring device, flipping the biometricmonitoring device over and back, or double-tapping the housing of thebiometric monitoring device, touching a surface area, or placing a bodypart near a proximity sensor.

There is generally a trade-off between speed and accuracy of biometricdata for biometric data such as step counts, cadence, and heart rate.This trade-off is further exacerbated by the limited power supply of aminiaturized BMD. This disclosure address this problem by providing BMDsthat have multiple device modes depending on operational conditions ofthe devices, e.g., motion intensity, device placement, and/or activitytype.

In some embodiments, a mode may be employed alone. In other embodiments,multiple modes may be combined at a particular instant. For example,when a user is wearing a BMD on her dominant hand, swinging her handsfreely, and walking up a flight of stairs, the device may simultaneouslyemploy a free motion mode (motion intensity), a stairclimbing mode(activity type), and a dominant hand mode (device placement). In someembodiments, one or more of the modes may be selected by automatictriggers as further described below. In some embodiments, one or more ofthe modes may be manually selected by the user through a user interface.

In some embodiments, data collected by a sensor device is communicatedor relayed to other devices. For example, while the user is wearing asensor device, the sensor device may calculate and store the user's stepcount using one or more sensors. The device then transmits datarepresentative of the user's step count to an account on a web servicesuch as computer, mobile phone, or health station where the data may bestored, processed, and visualized by the user. Indeed, the sensor devicemay measure or calculate a plurality of other physiological metrics inaddition to, or in place of, the user's step count. These include, butare not limited to, energy expenditure (e.g., calorie burned), floorsclimbed and/or descended, heart rate, heart rate variability, heart raterecovery, location and/or heading (e.g., through GPS), elevation,ambulatory speed and/or distance traveled, swimming lap count, swimmingstroke type, bicycle distance and/or speed, blood pressure, bloodglucose, skin conduction, skin and/or body temperature,electromyography, electroencephalography, weight, body fat, caloricintake, nutritional intake from food, medication intake, sleep periods(i.e., clock time), sleep phases, sleep quality and/or duration, pHlevels, hydration levels, and respiration rate.

In some embodiments, the sensor device may also measure or calculatemetrics related to the environment around the user such as barometricpressure, weather conditions (e.g., temperature, humidity, pollen count,air quality, rain/snow conditions, wind speed), light exposure (e.g.,ambient light, UV light exposure, time and/or duration spent indarkness), noise exposure, radiation exposure, and magnetic field.

Furthermore, the sensor device may calculate metrics derived from thecombination of the aforementioned data. For example, the sensor devicemay calculate the user's stress and/or relaxation levels through acombination of heart rate variability, skin conduction, noise pollution,and sleep quality. In another example, the sensor device may determinethe efficacy of a medical intervention (e.g., medication) through thecombination of medication intake, sleep and/or activity data. In yetanother example, the sensor device may determine the efficacy of anallergy medication through the combination of pollen data, medicationintake, sleep and/or activity data.

While the examples presented above illustrate the calculation of metricson a sensor device, they may be performed in part or wholly on anexternal system (e.g. web server, mobile phone, personal computer).Indeed, these examples are provided for illustration only and are notintended to be limiting or exhaustive. Further embodiments andimplementations of sensor devices can be found in U.S. patentapplication Ser. No. 13/156,304, titled “Portable Biometric MonitoringDevices and Methods of Operating Same” filed Jun. 8, 2011 which isentirely incorporated herein by reference.

Sensors are the tracking device's basic sensing hardware, e.g.,accelerometers, magnetometers, gyroscopes, PPG sensors, etc. Details ofvarious sensors and data types are further described hereinafter.

Sensor output data is a direct output from the tracking device'ssensors. Examples include acceleration, light intensity, etc. This datavaries with time and may contain constant or variable frequency and/oramplitude components. It may contain biometric information about theuser's activity and/or environmental information about ambientconditions that exist independently of the user's activity.

In some embodiments, sensor output data include raw data directlyobtained from the sensor without preprocessing. In some embodiments,sensor output data include data derived from the raw data afterpreprocessing.

Physiological metric is a physiologically relevant metric determinedfrom the tracking device's sensor output data. It is sometimes referredto as a biometric performance metric. Physiological metrics may becharacterized in various ways. For instance, it may be characterized by(1) basic units of physiological activity, e.g., steps, swimming stokes,pedal strokes, heartbeats, etc.; (2) increments of physiological output,e.g., pool laps, flights of stairs, heart rate, etc.; or (3) goals,including default or customized goals, e.g., 10,000 steps in a day.

“Activity type mode” as used herein refers to a device mode associatedwith a distinct user activity such as walking/running, rock climbing,sleeping, bicycling, swimming, etc. Each activity type mode may have anassociated trigger and sensor data processing algorithm.

“Trigger” is used with reference to event(s) that cause the trackingdevice to enter a particular device mode.

Some device operations may be unique to particular activity type modes.Examples include displayed content, display screen sequences, etc.

A “sensor data processing algorithm” is used in reference to acomputational process associated with a device mode. The sensor dataprocessing algorithm is used to convert sensor output data to aphysiological measure defined for the activity type. A tracking devicewill have multiple sensor data processing algorithms, each associatedwith one or more activity type modes. In some embodiments, differentmotion intensity modes have different sensor data processing algorithms.

Various motion intensity modes may be combined with an activity typemode. Motion intensity modes include two or more modes. In someembodiments, motion intensity modes have a high, an intermediate, and alow intensity mode. Each motion intensity mode having its own triggerand/or sensor data processing algorithm, and possibly other feature suchas display content. In one example, a motion intensity modedistinguishes high activity (e.g., walking) vs. low activity (e.g.,running). Another example distinguishes between walking with arms freelyswinging and walking with arms fixed to a stationary object such as atreadmill handle. Typically, the tracking device will determine the samephysiological metric for different motion intensity modes of the sameactivity type, so the device may determine a step count for both walkingwith arms freely swinging and walking with arms fixed.

Motion intensity modes are often deployed to address a device's currentenvironment or context. For example, the data processing algorithm for amotion intensity mode may be designed to improve the accuracy of theinformation output for a particular environment or context, and/or savepower in such environment or context. Some data processing algorithmsrequire more processing power and hence consume more energy, and suchalgorithms should be used only when needed for accuracy. As an example,activity sub-type modes producing periodic signals with large amplitudesor signal-to-noise ratios (SNRs) may be processed inexpensively in thetime domain, while other sub-type modes producing low amplitudes orsignal-to-noise ratios may need to be processed with a computationallydemanding algorithm in the frequency domain.

The term “monitor” is used with reference to a tracking device mode thatpresents monitored information about a distinct physiological activitysuch as heartbeats or steps. A monitor as a device mode is differentfrom an activity type mode as seen in a classic example of a heart ratemonitor, which is not specific to an activity type. A heart rate monitormay measure and/or present the basic unit of cardiac activity(heartbeat) and/or increments of cardiac activity (heart rate). Atracking device may have multiple monitors, each with its own triggerand sensor data processing algorithm. Other device operations that maybe specific to monitors include displayed content, display screensequences, etc. A monitor may have sub-modes with their own triggers anddata processing algorithms as discussed for activity type modes.

Device state mode is used with reference to operational modes associatedwith various states of the hardware. Examples include a high/low batterymode, a syncing mode, timer mode, stopwatch mode, annotation mode, etc.

FIG. 1 shows a Biometric monitoring device (BMD) that may implement themultimode functions disclosed herein. The BMD 100 in FIG. 1 includes ahousing 102 that contains the electronics associated with the biometricmonitoring devices 100. Among other sensors, the housing 102 includes amotion sensor. The BMD also has a button 104 to receive user inputthrough button presses. Under certain context, one kind of button pressreceived through button 104 may represent manual command to change themode of the BMD in manners described below. The BMD 100 also includes adisplay 106 that may be accessible/visible through the housing 102. Thecomponents that may be integrated in a BMD is further illustrated in aschematic diagram shown in FIG. 7 below.

FIG. 2 depicts another embodiment of a BMD having multimode functionsthat may be worn on a person's forearm like a wristwatch, much like aFitbit FLEX™ or FORCE™ Biometric monitoring device 200 has a housing 202that contains the electronics associated with the biometric monitoringdevice 200. A button 204 and a display 206 may be accessible/visiblethrough the housing 202. A wristband 208 may be integrated with thehousing 202.

Multimode Feature

When using a BMD to track physiological activities, the speed andaccuracy of the measurement are affected by various factors, e.g., thedevice placement, the types of the activity the user engages in, andcharacteristics of the user's motion, etc. For instance, a user may bewearing a BMD on her wrist of her dominant hand for pedometry purposes.She may be running on a treadmill while holding a handle bar andflipping a magazine occasionally. This scenario presents challenges toconventional methods and devices that track steps and exploration. Thefact that the user is holding the handle bar reduces the motion signalin her wrist that can be detected by the motion sensor of the BMD. Also,her occasional hand movements from flipping the magazine creates motionnoise, which the BMD may mistakenly interpreted as steps.

In some embodiments, methods and devices are provided to overcomedifficulties as in similar scenarios. In some embodiments, the BMD usespeak detection analysis for user activities that have high signal orsignal-to-noise ratio (SNR), because peak detection analysis is oftentime and energy efficient, requiring less data and processing, as wellas energy associated with the processing. Furthermore, the BMD usesperiodicity analysis for activities that have lower signal or SNR, whichis better at picking up relatively low signals and at filtering outmotion noise that don't have regular temporal patterns. In someembodiments, the BMD has the function to automatically trigger variousdevice modes to apply appropriate algorithms for analysis andprocessing. In some embodiments, signal periodicity is obtained byfrequency domain analysis. In some embodiments, the signal periodicitymay be obtained by time domain analysis. In some embodiments, frequencydomain analysis and time domain analysis may be combined to obtain theperiodicity.

FIG. 3 shows a flow chart of method 300 for tracking a user'sphysiological activity according to some embodiments. The method uses aworn biometric monitoring device having one or more sensors to provideoutput data indicative of the user's physiological activity. Method 300starts by analyzing sensor output data to determine that the user isengaged in a first activity that produces output data that has arelatively high SNR. See block 310. Method 300 proceeds to quantify aphysiological metric, e.g., step count or heart rate, by analyzing afirst set of sensor output data in the time domain. See block 320. Insome embodiments, the BMD includes a motion sensor and the sensor outputdata includes amplitude of acceleration. In some of such embodiments,the time domain analysis may involve peak detection of acceleration.Method 300 also involves analyzing subsequent sensor output data todetermine that the user is engaged in a second activity that produces arelatively low SNR in the subsequent sensor output data (in comparisonto the prior sensor output data). See block 330. Furthermore, method 300involves quantifying the physiological metric from a periodic componentof a second set of sensor output data by processing the second set ofsensor output data using a frequency domain analysis. See block 340. Insome embodiments, the frequency analysis involves spectral analysis todetect spectral peaks and harmonics. In other embodiments, the frequencyanalysis applies a frequency band filter to the data, and then appliespeak detection to the frequency filtered data to obtain periodicinformation in the second set of sensor output data. The peak detectionalgorithm may work on time domain data, albeit filtered in the frequencydomain. In some implementations, SNR is not calculated, rather thesensor output data is characterized by a process that classifies in amanner indicative of SNR. For example, a classifier may be used toclassify the data based on motion or signal strength by using input suchas acceleration amplitude or power and other characteristics ofaccelerometer output.

Method 300 applies time domain analysis to data with relatively highsignal (or SNR) and frequency analysis to data with relatively lowsignal. In certain embodiments, the method applies exclusively timedomain analysis to the high SNR data and at least some frequency domainanalysis to the low SNR data. In some embodiments, the BMD appliesdifferent motion intensity modes triggered by different motion intensitylevels measured by a motion sensor, which reflects different useractivity characteristics. The criterion that distinguishes the signallevel for the two analyses should reflect different characteristics ofthe user's activity, e.g., running with hand moving freely vs. runningwith hand holding a bar. Different measures of motion may be used as themetric for determining motion intensity modes, such as SNR, signalnorms, signal energy/power in certain frequency bands, wavelet scaleparameters, and/or a number of samples exceeding one or more thresholds.Different values may be used set to as criteria for relatively low andrelatively high signals. In some embodiments, a single value may be usedto separates the first and second activity. In some embodiments, a thirdactivity may be determined to have an activity level lower than thesecond activity (relatively low activity). The device may enter aninactive mode and not perform further analyses on the sensor outputdata.

In some embodiments, a sensor device can measure the user's activityintensity via pedometry. The sensor device can be implemented withsingle or multiple motion sensors that provide continuous or digitizedtime-series data to processing circuitry (e.g. ASIC, DSP, and/ormicrocontroller unit (MCU)). The processing circuitry runs algorithms tointerpret the motion signals and derive activity data. In the case of apedometer, the derived activity data comprises step counts. In someembodiments, the method analyzes motion data of multiple axis of amulti-axis motion sensor when the sensor output data signal isrelatively low. In some embodiments, the method analyzes motion data ofonly a single axis of a multi-axis motion sensor when the sensor outputdata signal is relatively high, which improves time and energyefficiency in computing the physiological metric.

Categories of Modes

This subsection outlines the different types of modes. Sectionshereinafter explain how various modes may be triggered, and howdifferent modes apply different analyses and processes to derivebiometric information. In some embodiments disclosed herein, BMDs havedifferent kinds of modes that are triggered by different conditions andassociated with different processing tailored for the conditions. Insome embodiments, the device modes are provided in various categories:motion intensity modes, device placement modes, activity type modes,device state modes, etc. In some embodiments, some modes from thedifferent categories may be combined for a particular condition. Forinstance, a semi-active motion intensity mode, a running activity typemode, and a dominant hand device placement mode may be combined for thescenario of running on treadmill when holding a handle bar describedabove.

Activity Type Modes

In some embodiments motion related activities are tracked by the BMD. Insome embodiments, the BMD applies different processing algorithms thedifferent activity types to provide speed and accuracy of biometricmeasurement and to provide activity specific metrics. For instance, BMDmay provide elevation and route difficulty level in a rock climbingmode, but it may provide speed and cadence in a running mode.

In some embodiments, activity type modes may include, but are notlimited to running, walking, elliptical and stair master, cardioexercise machines, weight training, driving, swimming, biking, stairclimbing, and rock climbing.

Motion Intensity Modes

In some embodiments, there may be two or more different motion intensitymodes. In some implementations, the BMD applies different processingalgorithms to the different motion intensity modes to optimize speed andaccuracy of biometric measurement and to provide activity specificmetrics. In some embodiments, three motion intensity modes may bedescribed in terms of three levels or ranges motion intensity measuredby a motion sensor. These are sometimes loosely characterized herein asactive mode, semi-active mode, and inactive mode. The algorithmicdeterminations of and the transitions between the modes, which enablestep counting in a continuous manner and subsequent measurement of theuser's biometric signals are further discussed herein. It should benoted that the three mode approach described herein is for illustration,and is not a limitation of the present inventions. There may be fewermodes (e.g., active and not active (e.g., car) in a two mode system) orgreater than 3 modes. Indeed, the number of modes may vary depending onthe user and the typical activities performed by the user. The number ofmodes may also change dynamically for each user depending on thelikelihood of them to participating in certain activities. For example,a highly active mode may be disabled when a user is detected to be atwork using a GPS. Description below provides further details abouttriggering events to enter different motion intensity modes. Often, themotion intensity modes are specific for a particular type of activitysuch as step counting.

Device Placement Modes

Sensor devices may infer users' activity levels algorithmically byprocessing the signal from sensors (e.g. motion, physiological,environmental, location, etc.). In the case of motion sensing, thesignal can be affected by the placement of the sensor device. Forexample, motion signatures of the dominant hand and non-dominant handare significantly different, leading to inaccurate estimation ofactivity levels from motion signals generated from the wrist, becauseusers can choose to mount the sensor device on either hand and switchfrom one hand to another hand based on their needs. A set of modalitiestake different placements into account so that accurate and consistentbiometric data measurement is enabled regardless of where users weartheir sensor device.

Placement modes may include but are not limited to user's pocket, belt,belt loop, waistband, shirt sleeve, shirt collar, shoe, shoelaces, hat,bra, tie, sock, underwear, coin pocket, other articles of clothing, andaccessories such as a helmet, gloves, purse, backpack, belt pack, fannypack, goggles, swim cap, glasses, sunglasses, necklace, pendant, pin,hair accessory, bracelet, wristband, upper arm band and earring, andequipment such as skis, ski poles, snowboard, bicycle, skates, andskateboard. Additional modes may include those listed above with theadditional specification of whether the location is on a dominant ornon-dominant limb and/or left or right side of the user's body (e.g.wrist band on the dominant, right hand side of the user's body).

Monitor and Device State Mode

In some embodiments, the BMD has different monitor modes. A monitor is atracking device mode that presents monitored information about adistinct physiological activity such as heartbeats or steps. A monitoras a device mode is different from an activity type mode as seen in aclassic example of a heart rate monitor, which is not specific to anactivity type. A heart rate monitor may measure and/or present the basicunit of cardiac activity (heartbeat) and/or increments of cardiacactivity (heart rate). A tracking device may have multiple monitors,each with its own trigger and sensor data processing algorithm. Otherdevice operations that may be specific to monitors include displayedcontent, display screen sequences, etc. A monitor may have sub-modeswith their own triggers and data processing algorithms as discussed foractivity type modes.

Device states are operational modes associated with various states ofthe hardware. Examples include a high/low battery mode, a syncing mode,timer mode, stopwatch mode, annotation mode, etc.

Triggers for Entering Activity Type Modes, Device Placement Modes, andMonitors

Manual Triggers

In some embodiments, users may manually trigger one or more modes of theBMD. In some embodiments, a user's direct interaction with the BMD(e.g., tap, push a button, perform a gesture, etc.) may trigger thedevice to enter particular activity type modes, device placement modes,and monitors. In some embodiments, a user may trigger the device toenter a mode by an interaction with a secondary device communicativelyconnected to the BMD as described herein after. For instance, a user mayselect an activity type mode from a list of options in a smart phoneapplication or a web-browser.

The modes of the sensor devices can be selected manually by a user.Multiple methods can be considered in setting the most applicable modein this case. In one embodiment, the mode selection may be wholly orpartially determined from information gathered during sensor devicepairing and from the user's online account. Each sensor device may bepaired with an online account or secondary computing device such as asmartphone, laptop computer, desktop computer, and/or tablet whichenables entry of and stores user-specific information including but notlimited to the user's placement preference. This user-specificinformation may be communicated to the user's activity monitoringdevice, via a wireless or wired communication protocol. For example, inembodiments where the sensor device may be worn on either wrist, theuser may select a dominant or non-dominant hand setting to tune thebiometric algorithms for the wearing location.

In some embodiments, the placement or activity type mode can be setthrough a user interface on the device. The user can set the modethrough an interface that includes the display, button(s), and/or touchscreen. The mode selection may be stored in local storage of the deviceor in a secondary electronic device in communication with the sensordevice including but not limited to a server.

Hand gestures observed via motion sensors can be used to set such modesas well. There can exist one-to-one correspondence to a mode with a handgesture so that a particular hand gesture (e.g., waving the device)triggers a mode. In addition, a sequence of hand gestures can be used toenter a mode, e.g., hand-waving motion followed by a figure eightmotion. In these cases, the user may receive a confirmation of the modethrough a secondary sensual stimulation such as a play pattern of avibration motor, or LED's.

Automatic Triggers

In addition to manual mode set-up, automated algorithms (e.g. machinelearning) can be applied to detect the placement and/or activity type.In some embodiments, tracking device sensor output contains a detectableactivity type signature. The BMD may automatically detect the activitytype signature and trigger the BMD to enter an activity type modecorresponding to the activity type signature. In some embodiments, a BMDinteracts with an external signal that triggers the BMD to enter anactivity type mode or a monitor. The external signal may be provided by,e.g., RFID tag or other short range communication probe/signal affixedto activity type related objects such as a bicycle handle or a climbinghold. In some embodiments, the external signal may be provided by theenvironment such as ambient light intensity.

In some embodiment, an automatic trigger is implemented using motionsensors only. Signatures of motion signals are significantly differentdepending on the placement of the sensor device. Even at the sameplacement location, each user's activities will be registered in motionsignals that have different characteristics in time domain as well as atransformed domain (including but not limited to the spectral domain).Therefore, a machine learning classification technique (e.g. decisiontree learning, Hidden Markov Model (HMM) and Linear DiscriminantAnalysisis) may be considered for this supervised learning. For off-linetraining, the data are collected and annotated according to theplacement of the sensor device and activity type of users. Features arethen extracted from the data in the time-domain as well as itstransformed representations including but not limited to Fouriertransform and wavelet transform. The features are then used to traincoefficients that determine the decision rules. This set of coefficientsmay be trained offline (e.g. on a cloud in post processing). The set ofcoefficients are then incorporated into the embedded system of thesensor device so as to determine user's device placement location andactivity type.

In some embodiments, additional sensors can be used in addition tomotion sensors to detect activities. Additional sensors may include, butare not limited to those further described hereinafter. The activitytypes can be statistically inferred from signals from the additionalsensors with or without motions signals. For example, an HMM can beutilized where the hidden states are defined to be the physicalactivities, and the observed states are subset or all of the sensorsignals. An example of using an additional sensor for automatic triggerof an activity type mode is automated swimming detection via pressuresensor by detecting a steep pressure increase or high pressure. GPS dataor GPS signal in combination with some signatures in motion signals canbe statistically modeled to detect user activities whose speed is adesirable metric of the activity (e.g. driving and biking).

In some embodiments modes may be automatically or semi-automatically(e.g. one or more, but not all steps of selecting a mode areautomatically performed) selected with the use of a short range wirelesscommunication as described in U.S. patent application Ser. No.13/785,904, titled “Near Field Communication System, and Method ofOperating Same” filed Mar. 5, 2013 which is entirely incorporated hereinby reference. In some embodiments, a radio device can be placed at aspecific location associated with the activity to be detected. Forinstance, an NFC chip can be attached to gym equipment. A gym user cantag the gym equipment with her NFC enabled sensor device before andafter the specific exercise. In one embodiment the NFC chip mounted onthe gym equipment may also transmit exercise data gathered from the gymequipment that can be used to correct and/or improve activity datameasured by the sensor device.

Even during an activity, the radio devices can be used to trackintensity and efficiency of the activity. One implementation of thisidea relates to NFC equipped holds for indoor climbing (e.g. rockclimbing). A climber must contact their hands and feet to the holds toclimb up, as well as the initial holds and final hold that define aroute (a route is a predefined area, path, and/or set of holds which canbe used in a climb and is typically given a rating corresponding to itsdifficulty). The sensor device or devices mounted on the users' hands,feet, and or other body parts communicate with NFC chips placed in ornear the holds. The information collected via the sensor devices areprocessed in the sensor device(s) and/or a cloud computing system toprovide a better understanding of the activity to the users. See Section4.a for detailed implementations and embodiments.

Pre-existing radio equipment can be utilized to detect a user activity.Modern cars are often equipped with Bluetooth (BT) technology. Thesensor device enabled with BT can pair with the car through BTcommunication protocol. Once the monitoring device and car are paired toeach other, a walk-in to the car will prompt syncing between the two,and the car will be able to transmit status and information on theuser's activity (e.g. driving for n hours at x mph).

Triggers for Entering Motion Intensity Modes

Manual Triggers

Similar to activity type modes and device placement modes, motionintensity modes may also be triggered by user interaction with thetracking device (e.g., tap, push a button, execute a gesture, etc.) orwith a secondary device (e.g., select in a smart phone application).

Automatic Triggers

In some embodiments, a tracking device or BMD's sensor output contains adetectable motion intensity signature. This motion intensity signaturemay be detected by the BMD and triggers the device to enter variousmotion intensity modes. Combinations of sensor outputs may be used. Theinput to the trigger algorithm may come directly or indirectly from thesensor output. For example, the input may be direct output from anaccelerometer or it may be processed accelerometer output such as a“sleep state” described below.

As explained above, certain activity characteristics are associated withdifferent levels of motion intensity detected by a motion sensor of aBMD worn by a user. In some conditions, a user is engaged in a movingactivity, but the user's limb wearing the BMD has reduced motion orlimited acceleration as compared to a regular moving activity withfreely moving limbs. For instance, the user may be running on atreadmill while holding a bar, walking while pushing a shopping cart, orwalking when carrying a heavy object. In such conditions, the motionintensity detected by the motion sensor may be greatly reduced. This isillustrated with data shown in FIGS. 4A-B. FIG. 4A shows accelerationdata in time domain (top panel) and frequency domain (bottom panel) forstationary, walking, and running activity for a user. FIG. 4B showssimilar data for stationary, running with hands on bars, and runningwith free hands. The top panel of FIG. 4A shows that running produceshigher acceleration signal than walking, which is in turn higher thanstationary. The top panel of FIG. 4B shows that running with free handsproduces the highest level of signal intensity, which is higher thanrunning with hands on bars, which is higher than stationary. Notably,running with hands on bars causes the acceleration signal to become moreirregular and noisier as compared to walking. With this lower signallevel and/or higher noise when running with hands on bars, it becomesdifficult to use peak detection analysis of time domain data to obtainstep counts. In some conditions, the BMD automatically analyses motionsignal provided by a motion sensor, and automatically switches motionintensity modes, which deploy different data processing algorithms toprocess motion data.

In one embodiment, the device can determine a mode of the device usingthe motion sensor signal strength. The motion sensor signal strengthcan, for instance, be determined by signal-to-noise ratio, signal norms(e.g. L1, L2, etc.), signal energy/power in certain frequency bands,wavelet scale parameters, and/or a number of samples exceeding one ormore thresholds. In some embodiments, accelerometer output power is usedto determine different motion intensity modes, where the power iscalculated as a sum of accelerometer amplitude values (or amplitudesquared values). In some embodiments, data from one axis, or two axes,or three axes of one or more motion sensor may be used to determine themotion intensity. In some embodiments, data from one axis are used forfurther analyses when the signal is relatively high, while data from twoor more axis are used for further analyses when the signal is relativelylow.

A motion intensity mode may be activated when the motion level is withina certain range. In the case of a pedometer sensor device, there may bethree different motion level ranges corresponding to three modes; activemode, semi-active mode, and inactive mode. The algorithmicdeterminations of and the transitions between the modes, which enablestep counting in a continuous manner and subsequent measurement of theuser's biometric signals are further discussed below. It should be notedthat the three mode approach described herein is for illustration, andis not a limitation of the present inventions. There may be fewer modes(e.g., active and not active (e.g., car) in a two mode system) orgreater than 3 modes. Indeed, the number of modes may vary depending onthe user and the typical activities performed by the user. The number ofmodes may also change dynamically for each user depending on thelikelihood of them to participating in certain activities. For example,a highly active mode may be disabled when a user is detected to be atwork using a GPS.

In some embodiments, in addition to or instead of real time or near realtime motion sensor data, previously processed and/or stored sensorinformation may be used to determine a motion intensity mode. In someembodiments, such previous information may include a record of motioninformation for a previous period (e.g., 7 days) at a fixed timeinterval (e.g., once per minute). In some embodiments, the previousinformation includes one or more of the following: a sleep score (awake,sleeping, restless, etc.), calories burned, stairs climbed, steps taken,etc. Machine learning may be used to detect behavior signatures from theprior information, which may then be used to predict the likelihood of asubject has certain activity levels at the present time. Someembodiments use one or more classifiers or other algorithm to combineinputs from multiple sources (e.g., accelerometer power and minutelyrecorded data) and to determine the probability that the user is engagedin an activity with certain characteristics. For instance, if a usertends to be working at a desk at 3 PM but doing shopping at 6 PM, theprior motion related data will show data pattern reflecting the user'stendency, which tendency can be used by the BMD in a classifier todetermine that the user is likely walking while pushing a shopping cartat the present time at 6:15 PM today.

In some embodiments, a clustering algorithm (e.g. k-means clustering,nearest neighborhood clustering, and expectation maximization) may beapplied to classified modes based on a-priori knowledge that users areprobably doing each activity (e.g., driving) for continuous periods oftime.

In some embodiments motion intensity modes may be automatically orsemi-automatically selected with the use of a short range wirelesscommunication as described above for automatic selection of activitytype modes and device placement modes.

Sensor Data Processing Distinctions-Activity Type Modes, DevicePlacement Modes, and Monitors

Users perform many types of activities over the course of the day.However, the sensor device is not necessarily optimized for all theactivities. Knowing the activity of a user for a given time enables asensor device to run one or more algorithms that are optimized for eachspecific activity. These activity specific algorithms yield moreaccurate data. According to some embodiments, in each activity typemode, different data processing algorithm may be applied to improveactivity metric accuracy and provide activity-specific biometrics.

A user may wear the BMD at different positions. Device placement modesmay be set manually or automatically as described above. In each deviceplacement mode, placement-specific algorithms are run in order toestimate biometrics of interest more accurately. A variant of theplacement-specific algorithms may be an adaptive motion signal strengththreshold that changes its value according to expected movements of thebody part. Adaptive filtering techniques may be used to cancel outexcessive movements of the body part using the placement mode as apriori. Pattern recognition techniques such as support vector machine orFisher's discriminant analysis can also be used to obtainplacement-specific classifiers, which will discern whether or not asignal or signatures of the signal are representative of the biometricsof interest.

Sensor Data Processing Distinctions-Motion Intensity Mode

Time Domain Analysis

In some embodiments, the BMD applies algorithms that process data in thetime domain. This is especially useful when for data with easy toidentify basic units of physiological activity in the time domain. Thisis typically used for data with high signal or SNR. In some embodiments,the time domain analysis includes peak detection of motion amplitudedata (e.g., acceleration). Returning to the example data discussed aboveand shown in the top panels of FIGS. 4A-B, one can see the conditionswhen motion signal or SNR is large in conditions when the user istalking or running with free hands. In conditions like these, a BMDemploys time domain analyses according to some embodiments.

In many embodiments, the time domain analysis is more time and energyefficient as compared to frequency domain analysis further describedbelow, which is suitable for data with insufficient signal or SNR. Peakdetection of motion data usually requires less amount of data to beanalyzed as compared to frequency analysis, therefore it has a lowerdemand for data amount and analyses. In various embodiments, the peakdetection operation may be performed using data collected from aduration in the order of magnitudes in seconds. In some embodiments, therange of data duration is about 0.5-120 seconds, or 1-60 seconds, 2-30seconds, or 2-10 seconds. In comparison, in some embodiments, afrequency analysis may use data of a longer duration than data used inpeak detection.

In one embodiment, a time-domain analysis can be applied to data ofrelatively low signal or SNR to find features associated withperiodicity and/or the period of the buffered motion sensor signal.These analyses may include, but are not limited to auto regressionanalysis, linear prediction analysis, auto regression moving averageanalysis, and auto/partial correlation analysis. One or more thresholdrules and conditional decision rules are then applied on the featuresand/or the coefficients of the analysis to detect periodicity andestimate the period, and subsequently biometrics of the user.

Frequency Domain Analyses

In some embodiments, algorithms operating in the frequency domain areused when time domain sensor data does not contain easy to identifybasic units of physiological activity. The problem often occurs becausethe periodic signals have relatively low amplitude and a peak detectionalgorithm may be insufficiently reliable. One example is step countingwith the tracking device on a user's wrist while the user is pushing astroller or shopping cart. Another example is step counting while theuser is on a treadmill or bicycling. Another example is step countingwhile a user is in a car. In this case, the frequency domain analysishelps us avoid counting steps when the user moves due to vibration ofthe ride such as when the car runs over a bump. A third example is whenthe user is walking while carrying a heavy object with the limb wearingthe BMD.

Referring to the example data discussed above and shown in the toppanels of FIG. 4B, acceleration signal or SNR is small when the user isrunning with hands on bars. It is difficult to use peak detection withthe data in shown in the top panel because the data is noisy and thepeaks are not reliable. However, the frequency components show spectralpeaks at about 65 Hz and 130 Hz in the bottom panel of FIG. 4B in thetwo subpanels for running with hands on bars. In conditions like these,a BMD employs frequency domain analyses according to some embodiments.

As mentioned above, a frequency analyses may use data buffered for alonger duration than data used in peak detection. In some embodiments,the range of data duration for frequency analysis is in the order ofmagnitudes in seconds to minutes. In some embodiments, the range isabout 1 second to 60 minutes, 2 seconds to 30 minutes, 4 seconds to 10minutes, 10 seconds to 5 minutes, 20 seconds to 2 minutes, or 30 secondsto 1 minute.

In some embodiments, the length of buffered motion signal may be setdepending on the desired resolution of the classification. Eachapplication of selection algorithms using motion intensity modes to thisbuffered motion signal returns a classified mode (e.g. semi-active anddriving mode) and step (cadence) counts for the segment of the motionsignal. Post processing may then be applied onto these resultant valuesin the processing circuitry of the sensor device and/or remoteprocessing circuitry (e.g. cloud server). In one embodiment, a simplefilter can be applied to the estimated steps (cadences) so as to removea sudden change in step (cadence) counts. In another instance, aclustering algorithm (e.g. k-means clustering, nearest neighborhoodclustering, and expectation maximization) may be applied to theclassified modes based on a-priori knowledge that users are probablydoing each activity (e.g., driving) for continuous periods of time.These updated modes from clustering are then used to update steps(cadences) for the given buffered motion signal.

In some embodiments, the BMD may have an active mode, a semi-activemode, and an inactive mode for motion intensity modes. In the activemode, the motion sensors of the sensor device detect acceleration,displacement, altitude change (e.g. using a pressure sensor), and/orrotations which can be converted into step counts using a peak detectionalgorithm. In inactive mode, the user is sedentary (e.g., sitting still)and the pedometer (via the motion sensors) does not measure any signalswhich have the signature of steps. In this case, no further computationsare performed to detect steps. In semi-active mode, the motion sensorsobserve some of the user's movements, but the motion signals do notpossess enough strong signatures of steps (e.g. a sequence of highamplitude peaks in a motion sensor signal that are generated by steps)to be able to accurately detect steps using the peak detectionalgorithm.

In semi-active mode, time- and/or frequency-domain analysis may beperformed on the buffered motion signal of a certain length to findfeatures associated with periodic movements such as steps. If anyperiodicity or features representing periodicity of the buffered motionsignal are found, the period is estimated and then interpreted asbiometrics of the user such as the average step rate of the bufferedmotion signal.

Frequency domain analysis could include techniques other than just usingFFT or spectrograms as illustrated in FIGS. 4A and 4B. For example, amethod may involve first band passing the time domain signal, and thenrunning a peak counter in the time domain. Other methods may be used toprocess data with frequency analyses, and the processed data may then befurther process to obtain periodicity or peak of signal.

In some embodiments, frequency-domain transformation/analysis may beperformed on the buffered motion signal using techniques including butnot limited to Fourier transform, e.g., fast Fourier transform (FFT),cepstral transform, wavelet transform, filterbank analysis, powerspectral density analysis and/or periodogram analysis. In oneembodiment, a peak detection algorithm in the frequency domain may beperformed to find spectral peaks that are a function of the average steprate of the buffered motion signal. If no spectral peaks are found, thealgorithm will conclude that the user's movements are not associatedwith ambulatory motion. If a peak or a set of peaks are found, theperiod of the buffered motion signal is estimated, enabling theinference of biometrics. In another embodiment, a statisticalhypothetical test, such as Fisher's periodicity test is applied todetermine if the buffered motion signal possess any periodicity andsubsequently, if it possess biometric information associated with theuser's activity. In yet another embodiment, the harmonic structure isexploited to test periodicity and/or estimate the period. For example, ageneralized likelihood ratio test whose parametric models incorporateharmonicity of the buffered motion signal may be performed.

In another embodiment, a set of machine-learned coefficients can beapplied onto a subset of frequency- and/or time-domain features that areobtained from frequency- and/or time-domain analysis described above. Alinear/non-linear mapping of an inner product of the coefficients andthe subset of spectral features then determines if the given bufferedmotion signal is generated from a user motion that involves someperiodic movements. The machine learning algorithm classifies motionsignals into two categories: signals generated from steps and signalsgenerated from activities irrelevant to steps.

With this semi-active mode algorithm, for example, steps can be detectedeven when the user is wearing the sensor device on his/her wrist andholding the handle bars of a treadmill while he/she is walking on thetreadmill. In the case that the buffered motion signal does not have thesignature of ambulatory motion, the buffered motion signal may bedisregarded without counting any steps to eliminate the chance ofincorrectly counting steps. For example, the motion signal of a userdriving over a bumpy road in the time domain will show a series of peaksof high amplitude which have a signature similar to that of steps. Apeak detection pedometer algorithm run on the time domain motion signalof driving on a bumpy road would cause the pedometer to count steps whenit should not. However, in the frequency-domain and/or in signals towhich an appropriate time-domain analysis is applied, the same motionsignal of driving on a bumpy road is unlikely to have signaturesassociated with ambulatory motion (e.g. signatures of periodicity). Whensignals represented in frequency domain and/or signals to which atime-domain analysis is applied do not have a signature of ambulatorymotion, steps are not counted as it can be assumed that the user is notactually walking or running.

Example-Motion Intensity Modes for the Walking/Running Activity Type

FIG. 5A is a flowchart showing process 500 for tracking step count usinga BMD according to some embodiments. The process automatically selectsmotion intensity modes, and applies different data processing algorithmsfor different motion intensity modes. The BMD has one or more sensorsproviding data indicative of the user's physiological activities,including motion data indicative of steps. The BMD senses motion of theuser using one or more motion sensors, which sensors are describedfurther below. See block 504. The BMD analyzes motion data provided bythe motion sensor to determine the motion intensity that is caused bythe user's activity. See block 506. In some embodiments as illustratedhere in FIG. 5B, the BMD determines three ranges of motion intensity:high, moderate, and low, respectively associated with an active mode, asemi-active mode and an inactive mode. In some implementations, theactive mode corresponds to a user running or walking with freely movinghands; the semi-active mode corresponds to the user running or walkingon a treadmill while holding fixed handlebars, typing at a desk, ordriving on a bumpy road; and the inactive mode corresponds to the userbeing stationary.

As stated above, some embodiments may employ more or fewer than threemotion ranges corresponding to more or fewer than three modes. Thespecific ranges of the different modes may defer for differentapplications or different users. The specific ranges may be supplied byoff-line prior knowledge in some embodiments. In some embodiments, thespecific ranges may be influenced by machine learning process thatselects the ranges having the best speed and accuracy for step countcalculation.

In process 500, if the BMD determines that the user is engaged in anactivity that allows the motion sensor to measure high motion intensity,the BMD may enter an active motion intensity mode. See block 508. Insome embodiments, in addition to current motion data, the BMD can alsouse other forms of motion related data in its analysis to determine themotion intensity modes. For instance, in some embodiments, the BMD canreceive prior data previously processed and/or store. Such data mayinclude sleep quality, step counts, calories burned, stairs climbed,elevation or distance traveled, etc. as described above. In someembodiments, the prior data were recorded at fixed intervals, such asevery minute, every 10 minutes, every hour, etc. The BMD may use one ormore classifiers to combine the current motion intensity signal and theprior motion related data to determine that the user is likely to beengaged in an activity producing high motion intensity signal, whichdetermination triggers the BMD to enter an active mode as a motionintensity mode. The BMD then applies a peak detection algorithm toanalyze the motion data. See block 514. The detected peaks andassociated temporal information provide data to calculate step count.

In some embodiments, the BMD may determine that the motion intensityfrom the motion sensor data is moderate as described above, thentriggers the BMD to enter the semi-active mode. See block 510. Themotion intensity range used to define the semi-active mode may be lowerthan the active mode and higher than the inactive mode. In someembodiments, the BMD applies frequency domain analysis and/or timedomain analysis to detect periodicity in the motion data. See block 516.In some embodiments, the BMD applies FFP to obtain frequency informationof the motion signal. Other frequency domain analysis and time domainanalysis described above are also applicable here. Using informationderived from the frequency domain or time domain analysis, the BMDdecides whether the data contains periodic information. See block 518.If yes, the BMD infers that the motion data is produced by the userengaging in walking or running on the treadmill, or some otheractivities with periodic movements of the limb wearing the BMD, such astyping at a desk. See block 520. In some embodiments, the BMD mayfurther apply one or more filters or classifiers to determine whetherthe periodic information is related to stepping action as furtherdescribed below. If so, the BMD calculates a step count using theperiodic information, e.g., a 1 Hz periodic motion lasting for 10seconds corresponds to a cadence of 60 steps per minute and 6 steps. Seeblock 524. If the DND determines that there is no periodic informationin the motion data, infers that the user is engaged in activities withthe regular motion, such as driving on a bumpy road. See block 522. Insome embodiments, the BMD may disregard any step counts that may haveotherwise accumulated during the corresponding period (e.g. steps fromtime domain analysis).

The BMD may enter into an inactive mode when motion intensity level islow. See block 512. The inactive mode may correspond to the user beingstationary. In some embodiments, the BMD does not further process themotion data when it is in an inactive mode.

FIG. 5B is a flowchart showing process 530 for a BMD to automaticallyselect modes for different user activity conditions according to someembodiments. The different modes then apply different analysis to obtainstep counts. Process 530 may be implemented as a sub-process of process500. Process 530 for switching modes uses motion intensity detected bymotion sensor and previously analyzed and/or recorded motion relatedinformation. In the embodiment shown here, the previous information isprocessed by a sleep algorithm. Process 530 starts with bufferingsamples of motion data. The amount of data buffered may depend ondifferent applications and conditions. In the process shown here,current motion data is buffered to determine whether the device shouldenter one of the motion intensity modes. This data for triggeringdifferent motion intensity modes may be the same or different from thedata that is used to analyze steps in the different modes. The durationof these two kinds of data may also be the same or different. In someembodiments, the BMD continuously buffers data samples in order todetermine whether to select, maintain, and/or change motion intensitymodes. The process proceeds to calculate the power of signal from thebuffered sample. In some embodiments the calculation is based on I₁norm, i.e. sum of the absolute values of the signal. See block 534.

Process 530 continues by determining whether the power of the signal isgreater than an empirically determining threshold σ as shown in block536. The threshold may be trained by machine learning algorithms in someembodiments to improve the algorithm for selecting the different modes,the machine learning training allows the BMD to obtain accurate stepcounts with high efficiency. In some embodiments, the empiricallydetermined threshold may be adjusted by the user or by knowledge basedon other users. If the process determines that the power of the signalis greater than the empirical threshold σ, the BMD is triggered to enterinto an active mode. See block 538. Then the BMD performs step countinganalysis in a manner similar to a classic pedometer as described aboveusing peak detection method. See block 540. If the process determinesthat the signal power is not greater than the empirical threshold σ,then in some embodiments, it uses a sleep algorithm to further analyzeif it should enter into a moderate or inactive mode. In someembodiments, the sleep algorithm analyzes prior motion relatedinformation to determine whether the user is likely to be asleep, awake,or moving when awake. In some embodiments, the prior motion relatedinformation may be information derived from motion, such as step counts,stairs climbed, etc., as further described herein. In some embodiments,if sleep algorithm determines that the user is likely sleeping, then itenters into an inactive mode. See block 548. In some embodiments, theBMD in the inactive mode performs no further analysis of the sensorsignal, which may help preserve battery of the BMD. See block 550.However if the sleep algorithm determines that the user is not sleeping,then the BMD enters into a moderate motion intensity mode. See block544. The BMD performs an FFT analysis of motion data in the frequencydomain to determine steps. Examples of some applicable frequencyanalyses are further described hereinafter.

In some embodiments, the BMD may implement the peak detection operationof 514 under active mode using process 610 shown in FIG. 6A. The processto implement peak detection to calculate step count in process 610starts with obtaining a new sample of motion data such as accelerationdata. In some embodiments, a sample is a digitized value recorded by asensor that is approximately linear to an analog signal to be measured.In some embodiments, the analog signal is acceleration (e.g., m/s²). Theduration of the sample may be chosen based on different considerationsas described above. In some embodiments, the new sample includesacceleration data for a duration of about 0.5-120 seconds, or 1-60seconds, 2-30 seconds, or 2-10 seconds.

The process then performs a peak detection analysis. See block 614. FIG.6B shows a process that may be used to implement peak detectionperformed in block 614 according to some embodiments. The process startsby waiting for data to fill a data buffer described above. See block650. Then the process involves looking for a global maximum of thebuffered data. See block 652. As shown in the diagram to the left ofblock 652, some embodiments may apply a rolling time window of durationN, which duration may be chosen as described above. The roller timewindow's starting and ending time may be designated as t and t+N asshown in the figure. The process searches for the global maximum of thedata in the rolling window. After the global max is computed, theprocess determines whether the global max is greater than an empiricallydetermined threshold θ. See block 654. If the global maximum is notgreater than the empirical threshold, then the process reverts towaiting for new data to fill the buffer as shown in operation 650. Ifthe global maximum is greater than the threshold, the process furtherdetermines whether the global maximum occurs at or near the center ofthe rolling time window. It the maximum is not at or near the center ofthe time window, the process determines that the peak is likely not astep, therefore the process reverts to waiting for new data to feel thebuffer is in operation 650. If the peak is centered on the buffered timewindow, the process determines that a peak is detected at or near t+N/2.

An alternative process may be applied for peak detection analysis, whichinvolves calculating the first derivative and finding any firstderivative with a downward-going zero-crossing as a peak maximum.Additional filters may be applied to remove noise from detected peaks.For instance, the presence of random noise in real experimental signalwill cause many false zero-crossing simply due to the noise. To avoidthis problem, one embodiments may first smooth the first derivative ofthe signal, before looking for downward-going zero-crossings, and thentakes only those zero crossings whose slope exceeds a certainpredetermined minimum (i.e., “slope threshold”) at a point where theoriginal signal exceeds a certain minimum (i.e., “amplitude threshold”).Adjustment of the smooth width, slope threshold, and amplitude thresholdcan significantly improve peak detection result. In some embodiments,alternative methods may be used to detect peaks. Process 610 thenproceeds to analyze whether the peak is associated with a step. Seeblock 160. This analysis may be performed by applying one or moreclassifiers or models. If the analysis determines that the peak is notassociated with a step, the process returns to obtaining a new sample asshown in block 612. If the analysis determines that the peak isassociated with a step, then the process increases the step count by 1.See block 618. Then the step counting process returns to obtaining a newsample shown in block 612. The step counting process continues on in thesame manner.

In some embodiments, the BMD may implement the data processing undersemi-active mode using process 620 shown in FIG. 6C. Process 620 startswith obtaining N new samples of motion data such as acceleration data.The N new samples in block 622 typically include more data than thesample in block 612 of process 610 for peak detection. In someembodiments, the samples include minutes' worth of data. The amount ofdata necessary depends on various factors as described above, and mayinclude various amounts in various embodiments. N depends on the dataduration and sampling rate, and is limited by the memory budget for stepcount analysis. Process 620 proceeds to perform a spectral analysis. Seeblock 624. In some embodiments, the spectral analysis is carried out byFourier transform (e.g., FFT) to show the power of various frequencies.Any peak on the frequency domain indicates there is periodicity in themotion data. For instance a peak at 2 Hz indicates periodic movement of120 times per minute. Process 620 then proceeds by examining if thespectral peak corresponds to steps. See block 626. This may be performedby applying one or more filters or classifiers. If the analysisdetermines that the spectral peak does not correspond to steps, then theprocess returns to block 622 to obtain N new samples. If the analysisdetermines that the spectral peak indeed relates to steps. Then theprocess increases the step count by M, wherein M is determined from thefrequency of the spectral peak and duration of the data. For instance,if the spectral peak occurs at 2 Hz, and N samples last for 60 seconds,then M would be 120 steps. In some embodiments, harmonics of the maximumpeak are also analyzed to assist determination of steps.

FIG. 6D shows details of a process that may be used to implement aspectral analysis applicable to operation 624 according to someembodiments. The process starts by applying a Hanning window the lastfor the time period of N, which prepares data for Fourier transform. Seeblock 660. Then the process performs a fast Fourier transformation insome embodiments. See block 662. The fast Fourier transform convertstime domain information into frequency domain information, showing thepower of various frequencies. The process then applies the peakdetection algorithm in the frequency domain to determine if there areany peaks at particular frequencies. Peak detection algorithms similarto those described above may be applied here to frequency domain data.If one or more peaks are detected for particular frequencies, theprocess infers that the data include a periodic component, which is usedto calculate steps. For instance, if a spectral peak occurs at 1 Hz, andN samples last for 30 seconds, then the process determines that 30 stepsoccurs in activity providing the data.

Example-Rock Climbing Activity Type Mode

In some embodiments, NFC or other short range wireless communicationsuch as Bluetooth, Zigbee, and/or ANT+ is used in a rock climbingsetting. A climber contacts their hands and feet to climbing holdsand/or climbing wall features to climb up, including the initial hold(s)and final hold(s) that define a route (a predefined area, path, and/orset of holds which can be used in a climb and is typically given arating corresponding to its difficulty). In one embodiment, active orpassive NFC enabled devices or tags are mounted on locations includingbut not limited to the user's hands, gloves, wrist bands feet, shoes,other body parts, wearable clothing, pocket, belt, belt loop, waistband,shirt sleeve, shirt collar, shoe, shoelaces, hat, bra, tie, sock,underwear, coin pocket, glove, other articles of clothing, accessoriessuch as a purse, backpack, belt pack, fanny pack, goggles, swim cap,glasses, sunglasses, necklace, pendant, pin, hair accessory wristband,bracelet, upper arm band, anklet, ring, toe ring, and earring tocommunicate with an active or passive NFC enabled chip or deviceembedded in, on, or near one or more climbing holds or carabineers forsport climbing routes. The information collected by the device ordevices on the climber and/or the climbing hold or wall is processed inthe device or devices on the climber and/or the climbing hold orclimbing wall and/or cloud computing system to provide data to the userand/or climbing gym about the user's climb.

In one embodiment, this data could be used to help the user keep trackof which climbs they have completed and/or attempted. The data may alsobe used by the climber to remember which holds and/or climbing wallfeatures they used and with which sequence they used the holds and/orclimbing wall features. This data could be shared with other climbers toaid them in completing part or the entire climbing route, compete, earnbadges and/or earn other virtual rewards. In some cases, climbers couldreceive only data from climbers of similar characteristics including butnot limited to height, weight, experience level (e.g. years climbing),strength, confidence or fear of heights and/or flexibility so as toimprove the relevance of the data in aiding them complete a climbingroute. In some cases, optional holds may be virtually added or takenaway from a virtual route to decrease or increase the difficulty of theroute. Upon the completion of a route, the climber may have the abilityto virtually share their achievement on online social networks. Virtualbadges may also be awarded for reaching a climbing achievement such ascompleting or attempting a climb or number of climbs of a specificdifficulty.

In another embodiment, climbers may wear a device which can detectfreefall using, for example, a motion sensor such as an accelerometer.Freefall detection data may be communicated wirelessly to a secondarydevice such as a smartphone, tablet, laptop, desktop computer, orserver. In one embodiment, a detection of freefall may cause anautomatic braking device to prevent the rope holding the climber fromfalling further. This may be used in addition to or instead of automaticmechanical fall stopping mechanisms and/or manually operated fallstopping mechanisms such as a belay device.

Freefall data may also be used in determining when a rope needs to beretired from use. Metrics including but not limited to the number offree fall events, time duration of free fall, maximum acceleration,maximum force (estimated using the weight of the climber), and/or energydissipated by the rope may be used in the calculation of when a ropeshould be expired. This data may also be presented to the user.

Freefall data may also be used to determine when climbers and/orbelayers are climbing unsafely. For example, if a climber takes a fallof a certain magnitude (as determined by one or more freefall metricsalready disclosed herein), the climbing gym staff may be alerted.

In another embodiment, climbing holds and or features may have embeddedor proximal auditory and/or visual indicators. These may be used insteadof the colored or patterned tape which is commonly used to indicatewhich hold and/or feature can be used in a climb. These indicators mayalso show which holds and what sequence of holds the user, one or moreother users, or one or more other users of similar characteristicsalready disclosed herein used on a previous climb.

In another embodiment, weight sensors integrated into the holds and/orfeatures may determine which holds and/or features were used during aclimb. The sequence of holds and/or wall features may be also determinedby a separate device in communication with the weight sensor enabledholds.

The climbing holds and/or wall features may also be used to determinewhich holds and/or wall features were used by feet, hands and/or otherbody parts. In one embodiment, they can also determine which hand orfoot (e.g. left or right) was used on which hold.

In one embodiment, visual characteristics of the holds or wall features(e.g. color, brightness, number of illuminated LED's) may change inreaction to having been used by a climber. This may be achieved with,for example, an RGB LED mounted inside a translucent hold and/or wallfeature. The visual indicators may also be located in proximity to thehold or wall features rather than being integrated into them directly.

Biometric Monitoring Device

It is desirable to have BMD that provide accurate analyses of metricsunder different measurement conditions while maintaining overallanalysis speed and energy efficiency. In some embodiments, the accuracy,speed, and efficiency may be achieved by deploying multiple modes thatprocess sensor output data differently. In some embodiments, the BMD mayswitch modes by automatic triggers as described above.

In some implementations, a BMD may be designed such that it may beinserted into, and removed from, a plurality of compatiblecases/housings/holders, e.g., a wristband that may be worn on a person'sforearm or a belt clip case that may be attached to a person's clothing.In some embodiments, the biometric monitoring system may also includeother devices or components communicatively linked to the biometricmonitoring device. The communicative linking may involve direct orindirect connection, as well as wired and wireless connections.Components of said system may communicate to one another over a wirelessconnection (e.g. Bluetooth) or a wired connection (e.g. USB). Indirectcommunication refers to the transmission of data between a first deviceand a secondary device with the aid of one or multiple intermediarythird devices which relay the data.

FIG. 7 depicts a generalized schematic of an example portable biometricmonitoring device, also simply referred to herein as “biometricmonitoring device,” or other device with which the various operationsdescribed herein may be executed. The portable biometric monitoringdevice 702 may include a processing unit 706 having one or moreprocessors, a memory 708, a user interface 704, one or more biometricsensors 710, and input/output 712. The processing unit 706, the memory708, the user interface 704, the one or more biometric sensors 710, andthe input/output interface 712 may be communicatively connected viacommunications path(s) 714. It is to be understood that some of thesecomponents may also be connected with one another indirectly. In someembodiments, components of FIG. 7 may be implemented as an externalcomponent communicatively linked to other internal components. Forinstance, in one embodiment, the memory 708 may be implemented as amemory on a secondary device such as a computer or smart phone thatcommunicates with the device wirelessly or through wired connection viathe I/O interface 712. In another embodiment, the User Interface mayinclude some components on the device such as a button, as well ascomponents on a secondary device communicatively linked to the devicevia the I/O interface 712, such as a touch screen on a smart phone.

The portable biometric monitoring device may collect one or more typesof biometric data, e.g., data pertaining to physical characteristics ofthe human body (such as step count, heartbeat, perspiration levels,etc.) and/or data relating to the physical interaction of that body withthe environment (such as accelerometer readings, gyroscope readings,etc.), from the one or more sensors 710 and/or external devices (such asan external blood pressure monitor). In some embodiments, the devicestores collected information in memory 708 for later use, e.g., forcommunication to another device via the I/O interface 712, e.g., asmartphone or to a server over a wide-area network such as the Internet.

Biometric information, as used herein, refers to information relating tothe measurement and analysis of physical or behavioral characteristicsof human or animal subjects. Some biometric information describes therelation between the subject and the external environment, such asaltitude or course of a subject. Other biometric information describesthe subject's physical condition without regard to the externalenvironment, such as the subject's step count or heart rate. Theinformation concerning the subject is generally referred to as biometricinformation. Similarly, sensors for collecting the biometric informationare referred to herein as biometric sensors. In contrast, informationabout the external environment regardless of the subject's condition isreferred to as environmental information, and sensors for collectingsuch information are referred to herein as environmental sensors. It isworth noting that sometimes the same sensor may be used to obtain bothbiometric information and environmental information. For instance, alight sensor worn by the user may function as part of aphotoplethysmography (PPG) sensor that gathers biometric informationbased on the reflection of light from the subject (such light mayoriginate from a light source in the device that is configured toilluminate the portion of the person that reflects the light). The samelight sensor may also gather information regarding ambient light whenthe device is not illuminating the portion of the person. In thisdisclosure, the distinctions between biometric and non-biometricinformation and sensors are drawn for organizational purposes only. Thisdistinction is not essential to the disclosure, unless specifiedotherwise.

The processing unit 706 may also perform an analysis on the stored dataand may initiate various actions depending on the analysis. For example,the processing unit 706 may determine that the data stored in the memory708 indicates that a goal step-count or cadence has been reached and maythen display content on a display of the portable BMD celebrating theachievement of the goal. The display may be part of the user interface704 (as may be a button or other control, not pictured, that may be usedto control a functional aspect of the portable biometric monitoringdevice). In some embodiments, the user interface 704 includes componentsin or on the device. In some embodiments, the user interface 704 alsoincludes components external from the device that are nonethelesscommunicatively linked to the device. For instance, a smartphone or acomputer communicatively linked to the BMD may provide user interfacecomponents through which a user can interact with the BMD.

In general, BMDs may incorporate one or more types of user interfacesincluding but not limited to visual, auditory, touch/vibration, orcombinations thereof. The BMD may, for example, display informationrelating to one or more of the data types available and/or being trackedby the biometric monitoring device through, for example, a graphicaldisplay or through the intensity and/or color of one or more LEDs. Theuser interface may also be used to display data from other devices orinternet sources. The device may also provide haptic feedback through,for instance, the vibration of a motor or a change in texture or shapeof the device. In some implementations, the biometric sensors themselvesmay be used as part of the user interface, e.g., accelerometer sensorsmay be used to detect when a person taps the housing of the biometricmonitoring unit with a finger or other object and may then interpretsuch data as a user input for the purposes of controlling the biometricmonitoring device.

The biometric monitoring device may include one or more mechanisms forinteracting with the device either locally or remotely. In oneembodiment, the biometric monitoring device may convey data visuallythrough a digital display. The physical embodiment of this display mayuse any one or a plurality of display technologies including, but notlimited to one or more of LED, LCD, AMOLED, E-Ink, Sharp displaytechnology, graphical display, and other display technologies such asTN, HTN, STN, FSTN, TFT, IPS, and OLET. This display could show dataacquired or stored locally on the device or could display data acquiredremotely from other devices or Internet services. The device may use asensor (for example, an Ambient Light Sensor, “ALS”) to control oradjust screen backlighting. For example, in dark lighting situations,the display may be dimmed to conserve battery life, whereas in brightlighting situations, the display may increase its brightness so that itis more easily read by the user.

In another embodiment, the device may use single or multicolor LEDs toindicate a state of the device. States that the device indicate mayinclude but are not limited to biometric states such as heart rate orapplication states such as an incoming message, a goal has been reached.These states may be indicated through the LED's color, being on, off, anintermediate intensity, pulsing (and/or rate thereof), and/or a patternof light intensities from completely off to highest brightness. In oneembodiment, an LED may modulate its intensity and/or color with theuser's cadence or step count.

In one embodiment, the use of an E-Ink display would allow the displayto remain on without the battery drain of a non-reflective display. This“always-on” functionality may provide a pleasant user experience in thecase of, for example, a watch application where the user may simplyglance at the device to see the time. The E-Ink display always displayscontent without comprising the battery life of the device, allowing theuser to see the time as they would on a traditional watch.

The device may use a light such as an LED to display the step count orheart rate of the user by modulating the amplitude of the light emittedat the frequency of the user's steps or heart rate. The device may beintegrated or incorporated into another device or structure, forexample, glasses or goggles, or communicate with glasses or goggles todisplay this information to the user.

The biometric monitoring device may also convey information to a userthrough the physical motion of the device. One such embodiment of amethod to physically move the device is the use of a vibration inducingmotor. The device may use this method alone, or in combination with aplurality of motion inducing technologies.

The device may convey information to a user through audio. A speakercould convey information through the use of audio tones, voice, songs,or other sounds.

Another embodiment the biometric monitoring device may transmit andreceive data and/or commands to and/or from a secondary electronicdevice. The secondary electronic device may be in direct or indirectcommunication with the biometric monitoring device. Direct communicationrefers herein to the transmission of data between a first device and asecondary device without any intermediary devices. For example, twodevices may communicate to one another over a wireless connection (e.g.Bluetooth) or a wired connection (e.g. USB). Indirect communicationrefers to the transmission of data between a first device and asecondary device with the aid of one or multiple intermediary thirddevices which relay the data. Third devices may include but are notlimited to a wireless repeater (e.g. WiFi repeater), a computing devicesuch as a smartphone, laptop, desktop or tablet computer, a cell phonetower, a computer server, and other networking electronics. For example,a biometric device may send data to a smartphone which forwards the datathrough a cellular network data connection to a server which isconnected through the internet to the cellular network.

In one embodiment, the secondary device which acts as a user interfaceto the biometric monitoring device may consist of a smartphone. An appon the smart phone may facilitate and/or enable the smartphone to act asa user interface to the biometric monitoring device. The biometricmonitoring device may send biometric and other data to the smartphone inreal-time or with some delay. The smart phone may send a command orcommands to the biometric device for example to instruct it to sendbiometric and other data in real-time or with some delay.

The smartphone may have one or multiple apps to enable the user to viewdata from their biometric device or devices. The app may by default opento a “dashboard” page when the user launches or opens the app. On thispage, summaries of data totals such as heart rate, the total number ofsteps, floors climbed miles traveled, calories burned, calories consumedand water consumed may be shown. Other pertinent information such aswhen the last time the app received data from the biometric monitoringdevice, metrics regarding the previous night's sleep (e.g. when the userwent to sleep, woke up, and how long they slept for), and how manycalories the user can eat in the day to maintain their caloric goals(e.g. a calorie deficit goal to enable weight loss) may also be shown.The user may be able to choose which of these and other metrics areshown on the dashboard screen. The user may be able to see these andother metrics on the dashboard for previous days. They may be able toaccess previous days by pressing a button or icon on a touchscreen.Alternatively, gestures such as swiping to the left or right may enablethe user to navigate through current and previous metrics.

The biometric monitoring device may be configured to communicate withthe user through one or more feedback mechanisms, or combinationsthereof, such as vibratory feedback, audio output, graphical output viaa display or light-emitting devices, e.g., LEDs.

In one example, while the user is wearing the biometric monitoringdevice 702, the biometric monitoring device 702 may measure and store auser's step count or heart rate while the user is wearing the biometricmonitoring device 702 and then subsequently transmit data representativeof step count or heart rate to the user's account on a web service likefitbit dot com, to a mobile computational device, e.g., a phone, pairedwith the portable biometric monitoring unit, and/or to a standalonecomputer where the data may be stored, processed, and visualized by theuser. Such data transmission may be carried out via communicationsthrough I/O interface 712. The device may measure, calculate, or use aplurality of physiological metrics including, but not limited to, stepcount, heart rate, caloric energy expenditure, floors climbed ordescended, location and/or heading (e.g., through GPS), elevation,ambulatory speed and/or distance traveled, swimming lap count, bicycledistance and/or speed, blood pressure, blood glucose, skin conduction,skin and/or body temperature, electromyography data,electroencephalographic data, weight, body fat, and respiration rate.Some of this data may be provided to the biometric monitoring devicefrom an external source, e.g., the user may input their height, weight,and stride in a user profile on a fitness-tracking website and suchinformation may then be communicated to the biometric monitoring devicevia the I/O interface 712 and used to evaluate, in tandem with datameasured by the sensors 710, the distance traveled or calories burned bythe user. The device may also measure or calculate metrics related tothe environment around the user such as barometric pressure, weatherconditions, light exposure, noise exposure, and magnetic field.

As mentioned previously, collected biometric data from the biometricmonitoring device may be communicated to external devices through thecommunications or I/O interface 712. The I/O or communications interfacemay include wireless communication functionality so that when thebiometric monitoring device comes within range of a wireless basestation or access point, the stored data automatically uploads to anInternet-viewable source such as a website, e.g., fitbit dot com. Thewireless communications functionality may be provided using one or morecommunications technologies known in the art, e.g., Bluetooth, RFID,Near-Field Communications (NFC), Zigbee, Ant, optical data transmission,etc. The biometric monitoring device may also contain wiredcommunication capability, e.g., USB.

Other implementations regarding the use of short range wirelesscommunication are described in U.S. patent application Ser. No.13/785,904, titled “Near Field Communication System, and Method ofOperating Same” filed Mar. 5, 2013 which is hereby incorporated hereinby reference in its entirety.

It is to be understood that FIG. 7 illustrates a generalizedimplementation of a biometric monitoring device 702 that may be used toimplement a portable biometric monitoring device or other device inwhich the various operations described herein may be executed. It is tobe understood that in some implementations, the functionalityrepresented in FIG. 7 may be provided in a distributed manner between,for example, an external sensor device and communication device, e.g.,an external blood pressure meter that may communicate with a biometricmonitoring device.

Moreover, it is to be understood that in addition to storing programcode for execution by the processing unit to effect the various methodsand techniques of the implementations described herein, the memory 708may also store configuration data or other information used during theexecution of various programs or instruction sets or used to configurethe biometric monitoring device. The memory 708 may also store biometricdata collected by the biometric monitoring device. In some embodiments,the memory may be distributed on more than one devices, e.g., spanningboth the BMD and an external computer connected through the I/O 712. Insome embodiments, the memory may be exclusively located on an externaldevice. With regard to the memory architecture, for example, multipledifferent classes of storage may be provided within the memory 708 tostore different classes of data. For example, the memory 708 may includenon-volatile storage media such as fixed or removable magnetic, optical,or semiconductor-based media to store executable code and related dataand/or volatile storage media such as static or dynamic RAM to storemore transient information and other variable data.

It is to be further understood that the processing unit 706 may beimplemented by a general or special purpose processor (or set ofprocessing cores) and thus may execute sequences of programmedinstructions to effectuate the various operations associated with sensordevice syncing, as well as interaction with a user, system operator orother system components. In some implementations, the processing unitmay be an application-specific integrated circuit.

Though not shown, numerous other functional blocks may be provided aspart of the biometric monitoring device 702 according to other functionsit may be required to perform, e.g., environmental sensingfunctionality, etc. Other functional blocks may provide wirelesstelephony operations with respect to a smartphone and/or wirelessnetwork access to a mobile computing device, e.g., a smartphone, tabletcomputer, laptop computer, etc. The functional blocks of the biometricmonitoring device 702 are depicted as being coupled by the communicationpath 714 which may include any number of shared or dedicated buses orsignaling links. More generally, however, the functional blocks shownmay be interconnected using a variety of different architectures and maybe implemented using a variety of different underlying technologies andarchitectures. The various methods and techniques disclosed herein maybe implemented through execution of one or more a sequences ofinstructions, e.g., software programs, by the processing unit 706 or bya custom-built hardware ASIC (application-specific integrated circuit)or programmed into a programmable hardware device such as an FPGA(field-programmable gate array), or any combination thereof within orexternal to the processing unit 706.

Further implementations of portable biometric monitoring devices can befound in U.S. patent application Ser. No. 13/156,304, titled “PortableBiometric Monitoring Devices and Methods of Operating Same” filed Jun.8, 2011, which is hereby incorporated herein by reference in itsentirety.

In some implementations, the biometric monitoring device may includecomputer-executable instructions for controlling one or more processorsof the biometric monitoring device to obtain biometric data from one ormore biometric sensors. The instructions may also control the one ormore processors to receive a request, e.g., an input from a button ortouch interface on the biometric monitoring device, a particular patternof biometric sensor data (e.g., a double-tap reading), etc., to displayan aspect of the obtained biometric data on a display of the biometricmonitoring device. The aspect may be a numerical quantity, a graphic, orsimply an indicator (a goal progress indicator, for example). In someimplementations, the display may be an illuminable display so as to bevisible when displaying data but otherwise invisible to a casualobserver. The instructions may also cause the one or more processors tocause the display to turn on from an off state in order to display theaspect of the biometric data. The instructions may also cause thedisplay to turn off from an on state after a predefined time periodelapses without any user interaction with the biometric monitoringdevice; this may assist in conserving power.

In some implementations, one or more components of 702 may bedistributed across multiple devices, forming a biometric monitoringsystem 702 spanning multiple devices. Such implementations are alsoconsidered to be within the scope of this disclosure. For instance, theuser interface 704 on a first device may not have any mechanism forreceiving physical input from a wearer, but the user interface 704 mayinclude a component on a second, paired device, e.g., a smart phone,that communicates wirelessly with the first device. The user interface704 on the smart phone allows a user to provide input to the firstdevice, such as providing user names and current location. Similarly, insome implementations, a biometric monitoring device may not have anydisplay at all, i.e., be unable to display any biometric datadirectly—biometric data from such biometric monitoring devices mayinstead be communicated to a paired electronic device, e.g., asmartphone, wirelessly and such biometric data may then be displayed ondata display screens shown on the paired electronic device. Suchimplementations are also considered to be within the scope of thisdisclosure, i.e., such a paired electronic device may act as a componentof the biometric monitoring system 702 configured to communicate withbiometric sensors located internal or external to the paired electronicdevice (such biometric sensors may be located in a separate module wornelsewhere on the wearer's body).

Biometric Sensors

In some embodiments, the biometric monitoring devices discussed hereinmay collect one or more types of physiological and/or environmental datafrom sensors embedded within the biometric monitoring devices, e.g., oneor more sensors selected from the group including accelerometers, heartrate sensor, gyroscopes, altimeters, etc., and/or external devices,e.g., an external blood pressure monitor, and may communicate or relaysuch information to other devices, including devices capable of servingas an Internet-accessible data sources, thus permitting the collecteddata to be viewed, for example, using a web browser or network-basedapplication. For example, while the user is wearing a biometricmonitoring device, the device may calculate and store the user's stepcount using one or more sensors. The device may then transmit the datarepresentative of the user's step count to an account on a web service,e.g., fitbit dot com, a computer, a mobile phone, or a health stationwhere the data may be stored, processed, and visualized by the user.Indeed, the device may measure or calculate a plurality of otherphysiological metrics in addition to, or in place of, the user's stepcount or heart rate.

The measured physiological metrics may include, but are not limited to,energy expenditure, e.g., calorie burn, floors climbed and/or descended,step count, heart rate, heart rate variability, heart rate recovery,location and/or heading, e.g., via GPS, elevation, ambulatory speedand/or distance traveled, swimming lap count, bicycle distance and/orspeed, blood pressure, blood glucose, skin conduction, skin and/or bodytemperature, electromyography data, electroencephalography data, weight,body fat, caloric intake, nutritional intake from food, medicationintake, sleep periods, sleep phases, sleep quality and/or duration, pHlevels, hydration levels, and respiration rate. The device may alsomeasure or calculate metrics related to the environment around the usersuch as barometric pressure, weather conditions, e.g., temperature,humidity, pollen count, air quality, rain/snow conditions, wind speed,light exposure, e.g., ambient light, UV light exposure, time and/orduration spent in darkness, noise exposure, radiation exposure, andmagnetic field. Furthermore, the biometric monitoring device, or anexternal system receiving data from the biometric monitoring device, maycalculate metrics derived from the data collected by the biometricmonitoring device. For instance, the device may derive one or more ofthe following from heart rate data: average heart rate, minimum heartrate, maximum heart rate, heart rate variability, heart rate relative totarget heart rate zone, heart rate relative to resting heart rate,change in heart rate, decrease in heart rate, increase in heart rate,training advice with reference to heart rate, and a medical conditionwith reference to heart rate. Some of the derived information is basedon both the heart rate information and other information provided by theuser (e.g., age and gender) or by other sensors (elevation and skinconductance).

The biometric sensors may include one or more sensors that evaluate aphysiological aspect of a wearer of the device, e.g., heart ratesensors, galvanized skin response sensors, skin temperature sensors,electromyography sensors, etc. The biometric sensors may also oralternatively include sensors that measure physical environmentalcharacteristics that reflect how the wearer of the device is interactingwith the surrounding environment, e.g., accelerometers, altimeters, GPSdevices, gyroscopes, etc. All of these are biometric sensors that mayall be used to gain insight into the activities of the wearer, e.g., bytracking movement, acceleration, rotations, orientation, altitude, etc.

A list of potential biometric sensor types and/or biometric data typesis shown below in Table 1, including motion and heart rate sensors. Thislisting is not exclusive, and other types of biometric sensors otherthan those listed may be used. Moreover, the data that is potentiallyderivable from the listed biometric sensors may also be derived, eitherin whole or in part, from other biometric sensors. For example, anevaluation of stairs climbed may involve evaluating altimeter data todetermine altitude change, clock data to determine how quickly thealtitude changed, and accelerometer data to determine whether biometricmonitoring device is being worn by a person who is walking (as opposedto standing still).

TABLE 1 Biometric Sensors and Data (physiological and/or environmental)Biometric Sensor Biometric data potentially Type measured Potentiallyderivable biometric data Accelerometers Accelerations experienced atRotation, translation, velocity/speed, location worn distance traveled,steps taken, elevation gained, fall indications, calories burned (incombination with data such as user weight, stride, etc.) GyroscopesAngular orientation, angular Rotation, orientation velocity, angularacceleration and/or rotation Altimeters Barometric pressure, temperatureAltitude change, flights of stairs (to calculate a more accurateclimbed, local pressure changes, altitude) submersion in liquid PulseOximeters Blood oxygen saturation (SpO2), Heart rate variability, stresslevels, heart rate, blood volume active heart rate, resting heart rate,sleeping heart rate, sedentary heart rate, cardiac arrhythmia, cardiacarrest, pulse transit time, heart rate recovery time, blood volumeGalvanic Skin Electrical conductance of skin Perspiration, stresslevels, Response Sensors exertion/arousal levels Global PositioningLocation, elevation, speed, Distance traveled, velocity/speed System(GPS)* heading Electromyographic Electrical pulses Muscletension/extension Sensors Audio Sensors Local environmental sound levelsLaugh detection, breathing detection, snoring detection, respirationtype (snoring, breathing, labored breathing, gasping), voice detection,typing detection Photo/Light Ambient light intensity, ambient Day/night,sleep, UV exposure, TV Sensors light wavelength watching, indoor v.outdoor environment Temperature Temperature Body temperature, ambientSensors environment temperature Strain Gauge Weight (the strain gaugesmay be Body Mass Index (BMI) (in Sensors located in a device remote fromconjunction with user-supplied the biometric monitoring device, heightand gender information, for e.g., a Fitbit ARIA ™ scale, and example)communicate weight-related data to the biometric monitoring device,either directly or via a shared account over the Internet) BioelectricalBody fat percentage (may be Impedance included in remote device, such asSensors ARIA ™ scale) Respiration Rate Respiration rate Sleep apneadetection Sensors Blood Pressure Systolic blood pressure, diastolicSensors blood pressure Heart Rate Sensors Heart rate Blood Glucose Bloodglucose levels Sensors Moisture Sensors Moisture levels Whether user isswimming, showering, bathing, etc.

In addition to the above, some biometric data may be calculated by thebiometric monitoring device without direct reference data obtained fromthe biometric sensors. For example, a person's basal metabolic rate,which is a measure of the “default” caloric expenditure that a personexperiences throughout the day while at rest (in other words, simply toprovide energy for basic bodily functions such as breathing, circulatingblood, etc.), may be calculated based on data entered by the user andthen used, in conjunction with data from an internal clock indicatingthe time of day, to determine how many calories have been expended by aperson thus far in the day just to provide energy for basic bodilyfunctions.

Physiological Sensors

As mentioned above, some biometric sensors can collect physiologicaldata, others can collect environmental data, and some may collect bothtypes of data. An optical sensor is an example of a sensor that maycollect both types of data. Many of the following sensors and dataoverlap with the biometric sensors and data presented above. They areorganized and presented below to indicate the physiological andenvironmental sources of information.

The biometric monitoring device of the present disclosure may use one,some or all of the following sensors to acquire physiological data,including the physiological data outlined in Table 2 below. Allcombinations and permutations of physiological sensors and/orphysiological data are intended to fall within the scope of the presentinventions. The biometric monitoring device of the present inventionsmay include but is not limited to one, some or all of sensors specifiedbelow to acquire the corresponding physiological data; indeed, othertype(s) of sensors may be employed to acquire the correspondingphysiological data, which are intended to fall within the scope of thepresent inventions. Additionally, the device may derive thephysiological data from the corresponding sensor output data, but is notlimited to the number or types of physiological data that it couldderive from said sensor.

TABLE 2 Physiological Sensors and Data Physiological SensorsPhysiological data acquired Optical Reflectometer Heart Rate, Heart RateVariability Potential embodiments: SpO2 (Saturation of PeripheralOxygen) Light emitter and receiver Respiration Multi or single LED andphoto diode Stress arrangement Blood pressure Wavelength tuned forspecific physiological Arterial Stiffness signals Blood glucose levelsSynchronous detection/amplitude Blood volume modulation Heart raterecovery Cardiac health Motion Detector Activity level detectionPotential embodiments: Sitting/standing detection Inertial, Gyro orAccelerometer Fall detection GPS Skin Temp Stress EMG Muscle tension EKGHeart Rate, Heart Rate Variability, Heart Rate Potential Embodiments:Recovery 1 lead Stress 2 lead Cardiac health Magnetometer Activity levelbased on rotation Laser Doppler Blood flow Power Meter Ultra Sound Bloodflow Audio Heart Rate, Heart Rate Variability, Heart Rate Recovery Laughdetection Respiration Respiration type - snoring, breathing, breathingproblems User's voice Strain gauge Heart Rate, Heart Rate VariabilityPotential embodiment: Stress In a wrist band Wet or Humidity sensorStress Potential embodiment: Swimming detection galvanic skin responseShower detection

In one exemplary embodiment, the biometric monitoring device includes anoptical sensor to detect, sense, sample, and/or generate data that maybe used to determine information representative of heart rate. Inaddition, the optical sensor may optionally provide data for determiningstress (or level thereof) and/or blood pressure of a user. In oneembodiment, the biometric monitoring device includes an optical sensorhaving one or more light sources (LED, laser, etc.) to emit or outputlight into the user's body and/or light detectors (photodiodes,phototransistors, etc.) to sample, measure and/or detect a response orreflection and provide data used to determine data which isrepresentative of heart rate (e.g., using photoplethysmography (PPG)),stress (or level thereof), and/or blood pressure of a user.

Environmental Sensors

The biometric monitoring device of the present inventions may use one,some or all of the following environmental sensors to, for example,acquire the environmental data, including environmental data outlined inTable 3 below. The biometric monitoring device is not limited to thenumber or types of sensors specified below but may employ other sensorsthat acquire environmental data outlined in the table below. Allcombinations and permutations of environmental sensors and/orenvironmental data are intended to fall within the scope of the presentinventions. Additionally, the device may derive environmental data fromthe corresponding sensor output data, but is not limited to the types ofenvironmental data that it could derive from said sensor.

The biometric monitoring device of the present inventions may use one ormore, or all of the environmental sensors described herein and one ormore, or all of the physiological sensors described herein. Indeed,biometric monitoring device of the present inventions may acquire any orall of the environmental data and physiological data described hereinusing any sensor now known or later developed—all of which are intendedto fall within the scope of the present inventions.

TABLE 3 Environmental Sensors and Data Environmental SensorsEnvironmental data acquired Motion Detector Location PotentialEmbodiments: Course Inertial, Gyro or Heading Accelerometer GPSPressure/Altimeter sensor Elevation, elevation Ambient Temp TemperatureLight Sensor Indoor vs outdoor Watching TV (spectrum/flicker ratedetection) Optical data transfer - initiation, QR codes, etc.ultraviolet light exposure Audio Indoor vs. Outdoor Compass HeadingPotential Embodiments: 3 Axis Compass

In one embodiment, the biometric monitoring device may include analtimeter sensor, for example, disposed or located in the interior ofthe device housing. In such a case, the device housing may have a ventthat allows the interior of the device to measure, detect, sample and/orexperience any changes in exterior pressure. In one embodiment, the ventprevents water from entering the device while facilitating measuring,detecting and/or sampling changes in pressure via the altimeter sensor.For example, an exterior surface of the biometric monitoring device mayinclude a vent type configuration or architecture (for example, a GORE™vent) which allows ambient air to move in and out of the housing of thedevice (which allows the altimeter sensor to measure, detect and/orsample changes in pressure), but reduces, prevents and/or minimizeswater and other liquids flow into the housing of the device.

The altimeter sensor, in one embodiment, may be filled with gel thatallows the sensor to experience pressure changes outside of the gel. Theuse of a gel filled altimeter may give the device a higher level ofenvironmental protection with or without the use of an environmentallysealed vent. The device may have a higher survivability rate with a gelfilled altimeter in locations including but not limited to those thathave high humidity, a clothes washer, a dish washer, a clothes dryer, asteam room, the shower, a pool, and any location where the device may beexposed to moisture, exposed to liquid or submerged in liquid.

Generally speaking, the techniques and functions outlined above may beimplemented in a biometric monitoring device as machine-readableinstruction sets, either as software stored in memory, asapplication-specific integrated circuits, field-programmablegate-arrays, or other mechanisms for providing system control. Suchinstruction sets may be provided to a processor or processors of abiometric monitoring device to cause the processor or processors tocontrol other aspects of the biometric monitoring device to provide thefunctionality described above.

Unless the context (where the term “context” is used per its typical,general definition) of this disclosure clearly requires otherwise,throughout the description and the claims, the words “comprise,”“comprising,” and the like are to be construed in an inclusive sense asopposed to an exclusive or exhaustive sense; that is to say, in a senseof “including, but not limited to.” Words using the singular or pluralnumber also generally include the plural or singular numberrespectively. Additionally, the words “herein,” “hereunder,” “above,”“below,” and words of similar import refer to this application as awhole and not to any particular portions of this application. When theword “or” is used in reference to a list of two or more items, that wordcovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list, and any combination ofthe items in the list. The term “implementation” refers toimplementations of techniques and methods described herein, as well asto physical objects that embody the structures and/or incorporate thetechniques and/or methods described herein.

There are many concepts and implementations described and illustratedherein. While certain features, attributes and advantages of theimplementations discussed herein have been described and illustrated, itshould be understood that many others, as well as different and/orsimilar implementations, features, attributes and advantages of thepresent inventions, are apparent from the description and illustrations.As such, the above implementations are merely exemplary. They are notintended to be exhaustive or to limit the disclosure to the preciseforms, techniques, materials and/or configurations disclosed. Manymodifications and variations are possible in light of this disclosure.It is to be understood that other implementations may be utilized andoperational changes may be made without departing from the scope of thepresent disclosure. As such, the scope of the disclosure is not limitedsolely to the description above because the description of the aboveimplementations has been presented for the purposes of illustration anddescription.

Importantly, the present disclosure is neither limited to any singleaspect nor implementation, nor to any single combination and/orpermutation of such aspects and/or implementations. Moreover, each ofthe aspects of the present disclosure, and/or implementations thereof,may be employed alone or in combination with one or more of the otheraspects and/or implementations thereof. For the sake of brevity, many ofthose permutations and combinations will not be discussed and/orillustrated separately herein.

1-38. (canceled)
 39. A method of tracking a user's physiologicalactivity using a worn biometric monitoring device having one or moresensors providing output data indicative of the user's physiologicalactivity, the method comprising: (a) analyzing a sensor output dataprovided by the worn biometric monitoring device to determine that theuser is engaged in a first activity that produces a relatively highsignal-to-noise ratio (SNR) in the sensor output data; (b) quantifying aphysiological metric by analyzing a first set of sensor output data in atime domain; (c) analyzing a subsequent sensor output data provided bythe worn biometric monitoring device to determine that the user isengaged in a second activity that produces a relatively low SNR in thesubsequent sensor output data; and (d) quantifying the physiologicalmetric from a periodic component of a second set of sensor output databy processing the second set of sensor output data using a frequencydomain analysis, wherein the quantifying in (d) differs from thequantifying in (b)
 40. The method of claim 39, wherein the quantifyingin (d) requires more computation per unit of duration of the sensoroutput data than the quantifying in (b).
 41. The method of claim 39,wherein the quantifying in (d) requires more computation per unit of thephysiological metric than the quantifying in (b).
 42. The method ofclaim 39, wherein the sensor output data comprise raw data directlyobtained from the one or more sensors without preprocessing and/or dataderived from the raw data after preprocessing.
 43. The method of claim39, wherein the worn biometric monitoring device comprises a wrist-wornbiometric monitoring device or an arm-worn biometric monitoring device.44. The method of claim 39, wherein analyzing the sensor output data in(a) or analyzing the subsequent sensor output data in (c) comprisescharacterizing the sensor output data based on a signal norm, a signalenergy/power in certain frequency bands, a wavelet scale parameter,and/or a number of samples exceeding one or more thresholds.
 45. Themethod of claim 39, further comprising analyzing a biometric informationpreviously stored on the worn biometric monitoring device to determinethat the user is engaged in the first or the second activity.
 46. Themethod of claim 39, wherein the first set of sensor output datacomprises data from only one axis of a multi-axis motion sensor, andwherein the second set of sensor output data comprises data from two ormore axis of the multi-axis motion sensor.
 47. The method of claim 39,wherein the one or more sensors comprise a motion sensor, and whereinanalyzing the sensor output data in (a) or analyzing the subsequentsensor output data in (c) comprises using a motion signal to determinewhether the user is engaged in the first activity or the secondactivity.
 48. The method of claim 47, wherein the first activitycomprises a free motion of a limb wearing the worn biometric monitoringdevice during the first activity.
 49. The method of claim 47, whereinthe second activity comprises a reduced motion of the limb wearing theworn biometric monitoring device during the second activity.
 50. Themethod of claim 49, wherein the second activity involves the userholding a substantially non-accelerating object with the limb wearingthe worn biometric monitoring device.
 51. The method of claim 39,wherein the analyzing in (b) comprises peak detection of a signal in thefirst set of sensor output data.
 52. The method of claim 39, wherein thefrequency domain analysis comprises: a Fourier transform, a cepstraltransform, a wavelet transform, a filterbank analysis, a power spectraldensity analysis and/or a periodogram analysis.
 53. The method of claim39, wherein the frequency domain analysis comprises filtering a timedomain signal with a frequency band pass filter, and then applying apeak detection analysis in the time domain.
 54. The method of claim 39,wherein the frequency domain analysis comprises finding any spectralpeak/peaks that is/are a function of an average step rate.
 55. Themethod of claim 39, wherein the frequency domain analysis comprisesperforming a Fisher's periodicity test.
 56. The method of claim 39,wherein the frequency domain analysis comprises using a harmonic toestimate a period and/or a test periodicity.
 57. The method of claim 39,wherein the frequency domain analysis comprises performing a generalizedlikelihood ratio test whose parametric models incorporate a harmonicityof a motion signal.
 58. The method of claim 39, wherein (b) and (d) eachcomprise: identifying a periodic component from the first or secondsensor output data; and calculating the physiological metric from theperiodic component from the first or second set of sensor output data.59. The method of claim 39, wherein the physiological metric comprises astep count.
 60. A method of tracking a user's physiological activityusing a worn biometric monitoring device having one or more sensorsproviding output data indicative of the user's physiological activity,the method comprising: (a) analyzing a sensor output data tocharacterize the sensor output data as indicative of a first activityassociated with a relatively high signal level or indicative of a secondactivity associated with a relatively low signal level; (b) processingthe sensor output data indicative of the first activity to produce afirst value of a physiological metric; and (c) processing the sensoroutput data indicative of the second activity to produce a second valueof the physiological metric, wherein the processing of (b) differs fromthe processing of (c).
 61. The method of claim 60, wherein theprocessing in (b) requires more computation per unit of the sensoroutput data duration than the processing in (c).
 62. The method of claim60, wherein the processing in (b) requires more computation per unit ofthe physiological metric than the processing in (c).
 63. A biometricmonitoring device comprising: one or more sensors providing output datacomprising information about a user's activity level when the biometricmonitoring device is worn by the user; a control logic configured for:(a) analyzing a sensor output data provided by the biometric monitoringdevice to determine that the user is engaged in a first activity thatproduces a relatively high SNR in the sensor output data; (b)quantifying a physiological metric by analyzing a first set of sensoroutput data in a time domain; (c) analyzing a subsequent sensor outputdata provided by the biometric monitoring device to determine that theuser is engaged in a second activity that produces a relatively low SNRin the subsequent sensor output data; and (d) quantifying thephysiological metric from a periodic component of a second set of sensoroutput data by processing the second set of sensor output data using afrequency domain analysis.
 64. The biometric monitoring device of claim63, wherein the quantifying in (d) requires more computation per unit ofduration of the sensor output data than the quantifying in (b).
 65. Thebiometric monitoring device of claim 63, wherein the quantifying in (d)requires more computation per unit of the physiological metric than thequantifying in (b).
 66. The biometric monitoring device of claim 63,wherein the biometric monitoring device comprises a wrist-worn biometricmonitoring device or an arm-worn biometric monitoring device.
 67. Thebiometric monitoring device of claim 63, wherein analyzing the sensoroutput data in (a) or analyzing the subsequent sensor output data in (c)comprises characterizing the sensor output data based on a signal norm,a signal energy/power in certain frequency bands, a wavelet scaleparameter, and/or a number of samples exceeding one or more thresholds.68. A biometric monitoring device comprising: one or more sensorsproviding output data comprising information about a user's activitylevel when the biometric monitoring device is worn by the user; acontrol logic configured to: (a) analyze a sensor output data tocharacterize the sensor output data as indicative of a first activityassociated with a relatively high signal level or indicative of a secondactivity associated with a relatively low signal level; (b) process thesensor output data indicative of the first activity to produce a firstvalue of a physiological metric; and (c) process the sensor output dataindicative of the second activity to produce a second value of thephysiological metric. wherein the process of (b) differs from theprocess of (c).